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What marginal returns now?

31 марта, 2020 - 02:12
Published on March 30, 2020 11:12 PM GMT

The returns to a lot of the discussion and analysis related to the outbreak certainly had both high potential and realized returns to that work.

Economics tells us we should expect diminishing marginal returns as additional efforts are applied.

I am being to wonder if the marginal returns now are positive or negative. If negative is that because we've just exhausted all the value to be created or is it due to misfocused efforts?


The Communist Norm in AGI Development

31 марта, 2020 - 01:55
Published on March 30, 2020 7:02 PM GMT

1. Introduction

This post outlines two models from the social epistemology of science explaining the emergence of the so-called ‘communist norm’, and looks at how such models can be utilised to understand research groups trying to develop AGI. In the rest of the introduction, I will try to provide some motivation for this post. Sections 2 & 3 will briefly outline the two models I'm looking at. Section 4 more directly tries to interpret such models in the context of AGI development. Section 5 concludes.

The social epistemology of science is an interdisciplinary subfield at the intersection of philosophy and economics, which utilises formal models to understand the incentive structure of science. Here, I focus on two models from this area which try to explain the emergence of the so-called ‘communist norm’ in scientific research. The communist norm is a norm to share all ‘substantive findings’ with the scientific community. The existence of this norm seems to be taken for granted in this literature, although the best piece of evidence I can find for its existence comes from Louis et. al (2001), who find, in a sample of nearly 2,000 geneticists, that 91% agree that one should share all of one's relevant data. I nevertheless take it for granted in this post.

I wanted to see whether understanding the emergence of the communist norm in science could be important for understanding the development of AGI. In many ways, one might think the incentive structures around the development of AGI (will, or does) parallel the incentive structures of academic science. Thus, one might think that looking at the incentive structures behind scientific research are a good starting point for looking at the incentive structures surrounding the development of AGI.

As the communist norm emerged in science, one can imagine the emergence of a similar ‘communist norm’ across research groups involved in AGI development, where research groups share all relevant research with one another. In the context of AGI, one might worry that sharing all research would speed up development alongside safety, potentially leading to a suboptimal scenario where capabilities improve faster than safety. If the incentive structures surrounding the development of AGI are appropriately similar to the incentive structures of science, then the models offered by Heesen and Strevens give different analyses of what we should expect among research groups trying to build AGI.

In the end, I tentatively conclude that Strevens' model is likely to be more useful for modelling AGI development races, and suggest that a social contract where AGI developers agree to share all safety-relevant information is incentive-compatible.

2. Strevens’ Paper: The Communist Norm and The Social Contract

Strevens aims to provide a ‘Hobbesian vindication’ of the communist norm in science, assuming self-interested, credit-maximising scientists. That is, his paper aims to provide the following:

  • A transformation of the communist norm into a social contract which behaviourally mirrors the norm.
  • A rationale for signing the social contract.

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src: local('MathJax_Size4'), local('MathJax_Size4-Regular')} @font-face {font-family: MJXc-TeX-size4-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Size4-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Size4-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Size4-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-R; src: local('MathJax_Vector'), local('MathJax_Vector-Regular')} @font-face {font-family: MJXc-TeX-vec-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-B; src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} n≥2 research programs, each with a discovery density, representing the probability that a given research group makes the discovery. For any research group F, let F be the event that F would make the discovery, with probability given by: P(F)=∫∞0f(t)dt. A program’s discovery probability is subjunctive -- that is, it is the probability that a scientific research program would make the discovery, given enough time. This is well-defined in the model even if a scientific research program never does, in fact, make the discovery. We say that a program’s power is equal to its discovery probability.

From the pool of potential winners (that is, programs with a non-zero probability of making the discovery), the actual winner is determined by each program’s race-clinching probability. A program's race-clinching probability is the conditional probability that a research program makes the discovery, given that another program also would have made the discovery. Formally, the race-clinching probability in a two-way race between F and G is given by C(F)=dfP(F∗G|FG)=P(F∗G)/P(FG), where F∗G is the event that, in a two-way race between F and G, F makes the discovery first, and FG is the event that both programs would make the discovery (Strevens calls such events 'live races').

It is assumed that the discovery density of each research program (that is, the probability that it would eventually make the discovery) is stochastically independent (i.e., P(FG)=P(F)P(G)). Strevens claims that such an assumption is innocent for his purposes, which is to prove that information exchange does not alter race-clinching probabilities. This is because race-clinching probabilities are of the form: P(F∗G)/P(FG), and that, when independence fails to hold (perhaps because of the success of one program is inversely correlated with another), it is likely to have roughly the same effect on the numerator as the denominator, and so will have negligible effect overall.

Research programs can engage in information exchange, which increases a program’s discovery probability by an amount proportional to the amount of information exchanged. The amount of information that a research program has to share is roughly proportional to its power. Strevens goes through the following ways information exchange might work:

(i) Inflation: information exchange could uniformly inflate each program’s discovery density (not necessarily by the same factor), by transforming f(t) to jf(t), for j>1.

(ii) Advancement: information exchange could provide each research program with an equal advancement, transforming their discovery densities from f(t) and g(t) to f(t+j), and g(t+j) respectively.

(iii) Compression/Rarefaction: information exchange could transform each program’s discovery density f(t) by turning into jf(jt), by the same factor j.

Conditional on our listed assumptions, cases (i) - (iii) can all be shown to have no effect on each program’s race-clinching probabilities. This is true whether the model involves a single time-period, or extended to involve a multiple-stage model of discovery (Strevens 2017, Section 6).

Strevens supplements his formal model with informal argumentation. In his model, Strevens shows that self-interested scientists should see positive value in sharing all information. This is because each program's probability of making the discovery increases through mutual information exchange, while not changing a program's race-clinching probability. Each program faces an increase in its discovery probability, while the probability that no one makes the discovery goes down.

However, the results of his model do not show that sharing all information is uniquely optimal, rather than (for example) a system that asks higher-powered programs to share more information. Strevens anticipates this objection, arguing that there are only a small number of practical contracts which could be ‘implemented at a reasonable cost given the scale’, and claims that: (a) sharing everything is better than sharing nothing, and (b) contracts which are more complicated (e.g., share equal amounts of information) are less likely to be implemented given their practical costs (such as deciding what counts as an appropriate unit of information).

From the conjunction of his formal results and informal reasoning, Strevens thinks he has thereby shown that self-interested scientists have an incentive to sign a contract pledging them to share all information.

3. Heesen’s Paper: Communism and The Incentive to Share

Heesen offers a game-theoretic model of sharing in science, called ‘The Intermediate Results Game’. In this model, an intermediate stage is any stage of a research project which, when completed, allows for a publishable result, and hence to some amount of credit for the scientist.

We assume n≥2 scientists compete to complete a research project, which has k intermediate stages. Whenever a scientist completes an intermediate stage, they face a choice: publish the result, or keep it to themselves. Publishing creates a benefit for the scientist, by giving them credit for that stage, as well as any preceding stages that were unpublished. The amount of credit for each stage j is given by cj>0, with total credit for all stages given by C=∑kj=1cj=1. In addition to providing credit for the scientist, the result also benefits the community, as the publication is now part of collective scientific knowledge. Such collective knowledge can help other scientists make discoveries at later stages. If the scientist refrains from publishing, this increases their probability that they will complete the next stage before competitors, and may thereby allowing them to claim credit for more stages later.

Heesen also makes the assumption that the probability it will take scientist i more than t time units takes to complete stage j is e−tλij, on the basis of empirical evidence concerning scientists’ productivity, which can be approximately fitted by a Poisson distribution. This justifies the assumption that waiting times are exponential, as this is just equivalent to assuming that scientists’ productivity is a nonstationary Poisson process. The parameter λ is to be interpreted as the speed at which a scientist works; 1/λij is the expected time scientist i takes to complete stage j. We also define:

Assumption 1: The speed parameters and the credit rewards have the following property: for every scientist i, and for each pair of stages j<j′: cj&#x3BB;ij≥cj′&#x3BB;ij′

That is, either the credit for each stage is proportional to its difficulty, or earlier stages are awarded more credit than later ones relative to their difficulty.

Granting Assumption 1, we get the following two results:

Theorem 1 (Heesen, 2017): In the intermediate results game with n≥2 scientists and k≥1 stages under perfect information, there is a unique backwards induction solution where scientists share information at all time periods. Moreover, there are no other behaviourally distinct equilibria in pure or mixed strategies.
Theorem 2 (Heesen, 2017): In the intermediate results game with n≥2 scientists and k≥1 stages under imperfect information, there is a unique, strict equilibrium, where scientists share information at every information set.

To say that an equilibrium is behaviourally distinct is to say that scientists make different decisions on nodes that are actually reached. Thus, from Heesen’s model we get the following conclusion: scientists have a credit incentive to share intermediate results, even though holding onto results allows one to have an advantage on completing the next research project. The conclusion of this model is therefore (at least in its original context) fairly optimistic: under a wide variety of situations, we do not need additional enforcement mechanisms to encourage scientific sharing, as that is what we should expect by default from rational, credit-maximising scientists.

4. Heesen, Strevens, and AGI Development

We now look at how these models can be used to model AGI development races, and raise questions about the extent to which such models can be used for this purpose.

4.1. Heesen’s Model

In Heesen’s model, the two theorems we looked at made use of the assumption that credit for each stage is proportional to its difficulty, or earlier stages are awarded more credit than later ones relative to their difficulty. I think this assumption is likely to fail in AGI development, where there is likely to be a particularly large benefit in being the team who completes the final step.

The failure of Assumption 1 is most obvious in hard takeoff scenarios, where one final insight leads to a large qualitative leap in intelligence. In this case, the ‘scientist’ (or, likely, research team in our scenario) who finished the last stage would get a huge share of the overall value, by being the research team who creates the AGI. This may be because the final step is more difficult, but there is no guarantee that this will be true. It is also plausible that Assumption 1 would fail in a variety of soft takeoff scenarios. Even under soft takeoff scenarios, it seems plausible to expect most of the value (or 'credit' in the original model) to be captured towards the end of the research project, as capabilities continue improving. That alone would also be enough to falsify Assumption 1, as teams who complete later stages would gain a disproportionate share of overall value, purely because such stages are later (and not because they are more difficult). I thus think there are good reasons to believe that, in the context of AGI development, we would not expect sharing by default.

The failure of Heesen’s model to apply to many cases of interest might be seen as positive, if we were initially worried that something like the communist norm naturally emerging in scenarios where credit was awarded for intermediate results. Obtaining credit for intermediate results seems plausible in the context of developing AGI, where, presumably, one would gain some level of outside prestige by through the contribution of (for example) novel mathematical results along the way to developing AGI. However, the failure of Heesen's model to be useful for (a sufficiently wide variety of) our purposes is likely to mean that, if we want to incentivise the sharing of safety techniques, we cannot expect this to emerge by default. The need to work on proposals to properly incentivise safety is (for most of us) unsurprising.

4.2. Strevens’ Model

Like Heesen’s model, some assumptions only questionably carry over to the case of AGI development: I focus here on the structure Strevens thinks information sharing should take: crucially, under Strevens' model, race-clinching probabilities are changed under information sharing, on the condition that information sharing compresses or advances one program’s discovery density more than another’s. In other words, Strevens' main result (that information sharing leaves race-clinching probabilities unchanged) fails to hold when information sharing unequally speeds up one research program's discovery density relative to another's.

In the context of AGI development, it seems plausible that one research program is likely to gain more from an information exchange than another. However, one might think that, even if this is true, each research program will, ex ante, have no reason to think they are the high-powered research program. Anecdotally, I think this is somewhat implausible. I imagine that successful groups tend to be lead by those who believe that they are more likely to succeed than average, and so a contract which relies on actively not believing that seems unlikely to emerge naturally.

I think the considerations above undercut the case for the communist norm emerging as a game-theoretic equilibrium in the case of AGI development, or as a contract independently drawn up by AGI developers. I do not think this conclusion should bother us too much, as it is (at best) dubious as to whether total information sharing would be socially optimal, given that total information sharing would also be likely to speed up the development of AI capabilities.

However, I don’t think my above criticisms of Strevens' model threaten the attractiveness of a contracted commitment to share all safety-relevant information. This would be a local version of the communist norm, rather than (as is allegedly the case in science) a global version of the communist norm. While some exchanges of ‘safety-relevant information’ might help another team improve their capabilities (hence changing the race-clinching probabilities), it seems unlikely that any research team would believe that sharing only information relevant to safety is likely to alter their ex ante estimates of the relative race-clinching probabilities. Assuming each research group has strictly positive concern for safety, each research team will be incentivised to commit to a safety-specific communist norm.

Strevens' model licenses a further optimistic conclusion concerning the development of AI capabilities relative to safety; if developers signed a contract to share all safety relevant information, then, as sharing information does not (in general) maximise credit, we should be less concerned that developers will, of their own accord, decide to engage in a process of free information sharing about non-safety relevant information. Although there were doubts about other parts of Strevens’ model, the basic setup as a prisoner’s dilemma seems to better capture the case of AGI development than Heesen’s model.

If I am right in my interpretation of Strevens’ model, then there are still possible situations under which research teams would be incentivised to renege on their promises, such as a case where sharing relevant safety information involves something which would improve the capabilities of another team, making it comparatively less likely that they will be the first to develop AGI. The possibility of such cases speaks to the need to look for mechanisms to effectively enforce adherence to a contract committed to sharing all safety-relevant information, or (potentially) further research on robust mechanisms of cooperation.

One possible enforcement mechanism would be to have firms commit to an underlined public statement, as discussed by Christiano. If firms all signed a contract agreeing to have such a public statement (including statements about commitments to share all relevant safety information), then this might provide a way of easily enforcing commitment without the need for resource-intensive outside policing.

5. Conclusions and Further Caveats

Looking at these two models has given me some insight into AGI development races. In particular, I feel like I am now more confident in the following claims:

  • An unrestricted version of the communist norm is unlikely to emerge endogenously in the course of AGI development.
  • A contract whereby all AGI developers agree to sign a pledge to share all safety relevant information is incentive compatible.

I am unsure whether such claims were obvious to those more familiar with literature on macrostrategy. I would thus be thankful to commenters who can point out examples of these claims in the existing macrostrategy literature. Moreover, I should stress that, although such updates are positive, such views are still reasonably tentative.

I end with a caveat. This piece has discussed a variant of the ‘communist norm’ applied to the development of AGI. I have suggested that the emergence of something like the communist norm in science (that is, the existence of a norm to share all relevant data) could be bad, but have mostly uncritically suggested that we could implement a safety-specific communist norm, where competing developers have to share all information relevant to safety research. I don't think I'm saying anything too controversial here: as Bostrom (2017) says, ‘openness about values, goals, and governance structures is generally welcome’.

That said, I feel compelled to mention that there is at least some tentative evidence that, among some network structures, less information can be better. For instance, Zollman (2007, 2010) finds that, among rational Bayesian agents who communicate with each other on a network structure, sparsely connected networks can sometimes epistemically outperform more densely connected networks. While I am not convinced that such results are sufficiently robust to changes in parameter values (see, for example, (Rosenstock et. al 2017)), a review of related results would have to be helpful before committing to a contract to share all safety-relevant information.


Categorization of Meta-Ethical Theories (a flowchart)

30 марта, 2020 - 20:14
Published on March 30, 2020 4:36 AM GMT

Hi folks! Been a LessWrong lurker for a while. Here's a little project I'm excited about, which has been useful in organizing my thoughts on meta-ethics.

This piece is a walk-through of an original flowchart (made with lots of help from friends) that categorizes the major meta-ethical positions. It lays out the points where different meta-ethical theories diverge and gives a brief intro to each major theory. I think it's a nice tool for getting a sense of the broadest strokes of academic meta-ethics, and being able to hold the different theories in your head.




30 марта, 2020 - 14:11
Published on March 30, 2020 11:11 AM GMT

This post is about trauma and trying to heal from it. Trigger warning: Suicide and self harm.


Resource for the mappings between areas of math and their applications?

30 марта, 2020 - 09:00
Published on March 30, 2020 6:00 AM GMT

One of the things that I want to do while I'm in self-quarantine is to learn more math.

One thing that would be really helpful for me is a mapping between areas of math and the technical subfields that use that math.

I'd want to be able to go in both directions.

From math to application: It's been recommended to me (and it fits my experience so far), that whenever I'm learning new math, I should learn an application of that math in parallel. For instance, when studying calculus, study mechanics at the same time. The applied field gives motivation and grounding to the math. It would be really good if I could take any given math course, and quickly get a list of all the fields that make use of it.

From technical field to mathematical prerequisite: There are lots of bits of science that I am interested in getting a technical understanding of (areas of neuroscience, and psychology, and machine learning, and physics, and economics). But very often I don't know where to start. I know that I am missing some of the prerequisites that these field's methods depend on. But I don't usually know what those prerequisites are. It would be helpful if I could take an area that I am interested in, and quickly backtrack to the math it is using.

Is there any existing resource that captures this information?

If not, how could I go about this, in each case?




The Real Standard

30 марта, 2020 - 06:09
Published on March 30, 2020 3:09 AM GMT

Long-delayed followup To: Roleplaying As Yourself

(Another simple intuition pump, this one especially useful for effective altruists who are struggling with wanting to do more or worrying they're not doing enough.)

Previously I wrote about a mental tool for prompting good consequentialist reasoning: ask yourself what a skilled alien roleplayer (here Gurgeh, from Player of Games) would do if they were controlling you, had to take only actions you could plausibly take, and scored points for achieving your goals.

This also serves as a standard for comparing your own actions, though as an aspiration rather than as an expectation.

The reason I mention this is that a good number of people in the rationality and effective altruism communities suffer from scrupulosity, the sense of guilt for not living up to an unattainable standard of conduct. And if we're going to speak to that sense, we need to start by getting the right standard of excellence to bargain with.

(If you're feeling scrupulous about altruism in particular, then you can imagine that Gurgeh gets points for achieving only your altruistic aims, though he's still constrained by your actual needs - he wouldn't steer you into a burnout, that wouldn't maximize his score.)

This standard is insanely daunting. Fortunately, it's not fair to ask you to meet it.

After all, you're not perfectly altruistic, and the other parts get to bargain too.

In Nobody Is Perfect, Everything Is Commensurable, Scott suggests that we deal with scrupulosity by letting ourselves be okay with the standard of giving 10% of our output to the most effective charitable causes. He runs into a bit of a problem when dealing with the fact that people are in different places in their careers (and that a tenth of one's income can be a large or small chunk of one's disposable income), and punts on the question a bit:

If you make $30,000 and you accept 10% as a good standard you want to live up to, you can either donate $3000 to charity, or participate in political protests until your number of lives or dollars or DALYs saved is equivalent to that.

I think this is the right place to introduce the alien gamer roleplaying your character. Are you building intangible expertise or career capital? Gurgeh notices the high payoff in later rounds of the game from these resources, and would be happy to forgo a little more short-term impact if your time/money/attention can translate into those resources more effectively. Are you torn between multiple opportunities to do good? Gurgeh checks once to see if there's a synergy between them (a way to get a higher combined total than he would optimizing for either alone), and if not, he ruthlessly picks the one that translates more efficiently into points, and doesn't feel bad about leaving behind a less efficient path.

So here's my suggestion:

Figure out the expected score that you'd actually expect Gurgeh to get in "The Altruistic You Experience", then consider ways to achieve at least one-tenth of that score, and let that be your target for moral achievement.

This is still a really high standard, one that few achieve! It almost surely isn't enough to take your default path in life while giving even 50% of your income to the best charity. It may require you to change your career, your social circles, your everyday habits. It may ask you to do lots of self-experimentation, with the corresponding expectation of frequent failure.

But it at least leaves more slack for your own flourishing than attempting to achieve the altruistic high score. It lets you seek a way of achieving excellence that satisfies your other wants and needs well. Maybe you don't take your altruistic best option if your second best is much more personally fulfilling; maybe you go ahead and splurge on something big every now and then. But you don't lose sight of your aspiration.

I just want to emphasize:

It's okay to give yourself more happiness and more leisure than you need in order to be effective. It's okay to care about your own well-being, and that of your family and friends, than that of strangers in far-off lands or times.

It's okay to be mostly selfish. Just be strategic about the altruistic part.


How many people have died in China from Covid-19?

30 марта, 2020 - 06:06
Published on March 30, 2020 3:06 AM GMT

Consider forming a model before researching, then researching and seeing how it changes.


For those of us living in the middle of nowhere, any cool meetup groups we can finally participate in during our lockups?

30 марта, 2020 - 04:41
Published on March 30, 2020 1:41 AM GMT

Are there any rationality groups in Silicon Valley/Tokyo/Berlin/etc that have transitioned to online meetings? It might be fun for those of us who don't live in the "cool places" to be able to participate at this time. I'd love it if folks could share some links, if they exist!


My current framework for thinking about AGI timelines

30 марта, 2020 - 04:23
Published on March 30, 2020 1:23 AM GMT

At the beginning of 2017, someone I deeply trusted said they thought AGI would come in 10 years, with 50% probability.

I didn't take their opinion at face value, especially since so many experts seemed confident that AGI was decades away. But the possibility of imminent apocalypse seemed plausible enough and important enough that I decided to prioritize investigating AGI timelines over trying to strike gold. I left the VC-backed startup I'd cofounded, and went around talking to every smart and sensible person I could find who seemed to have opinions about when humanity would develop AGI.

My biggest takeaways after 3 years might be disappointing -- I don't think the considerations currently available to us point to any decisive conclusion one way or another, and I don't think anybody really knows when AGI is coming. At the very least, the fields of knowledge that I think bear on AGI forecasting (including deep learning, predictive coding, and comparative neuroanatomy) are disparate, and I don't know of any careful and measured thinkers with all the relevant expertise.

That being said, I did manage to identify a handful of background variables that consistently play significant roles in informing people's intuitive estimates of when we'll get to AGI. In other words, people would often tell me that their estimates of AGI timelines would significantly change if their views on one of these background variables changed.

I've put together a framework for understanding AGI timelines based on these background variables. Among all the frameworks for AGI timelines I've encountered, it's the framework that most comprehensively enumerates crucial considerations for AGI timelines, and it's the framework that best explains how smart and sensible people might arrive at vastly different views on AGI timelines.

Over the course of the next few weeks, I'll publish a series of posts about these background variables and some considerations that shed light on what their values are. I'll conclude by describing my framework for how they come together to explain various overall viewpoints on AGI timelines, depending on different prior assumptions on the values of these variables.

By trade, I'm a math competition junkie, an entrepreneur, and a hippie. I am not an expert on any of the topics I'll be writing about -- my analyses will not be comprehensive, and they might contain mistakes. I'm sharing them with you anyway in the hopes that you might contribute your own expertise, correct for my epistemic shortcomings, and perhaps find them interesting.

I'd like to thank Paul Christiano, Jessica Taylor, Carl Shulman, Anna Salamon, Katja Grace, Tegan McCaslin, Eric Drexler, Vlad Firiou, Janos Kramar, Victoria Krakovna, Jan Leike, Richard Ngo, Rohin Shah, Jacob Steinhardt, David Dalrymple, Catherine Olsson, Jelena Luketina, Alex Ray, Jack Gallagher, Ben Hoffman, Tsvi BT, Sam Eisenstat, Matthew Graves, Ryan Carey, Gary Basin, Eliana Lorch, Anand Srinivasan, Michael Webb, Ashwin Sah, Yi Sun, Mark Sellke, Alex Gunning, Paul Kreiner, David Girardo, Danit Gal, Oliver Habryka, Sarah Constantin, Alex Flint, Stag Lynn, Andis Draguns, Tristan Hume, Holden Lee, David Dohan, and Daniel Kang for enlightening conversations about AGI timelines, and I'd like to apologize to anyone whose name I ought to have included, but forgot to include.

Table of contents

As I post over the coming weeks, I'll update this table of contents with links to the posts, and I might update some of the titles and descriptions.

How special are human brains among animal brains?

Humans can perform intellectual feats that appear qualitatively different from those of other animals, but are our brains really doing anything so different?

How uniform is the neocortex?

To what extent is the part of our brain responsible for higher-order functions like sensory perception, cognition, and language[1], uniformly composed of general-purpose data-processing modules?

How much are our innate cognitive capacities just shortcuts for learning?

To what extent are our innate cognitive capacities (for example, a pre-wired ability to learn language) crutches provided by evolution to help us learn more quickly what we otherwise would have been able to learn anyway?

Are mammalian brains all doing the same thing at different levels of scale?

Are the brains of smarter mammals, like humans, doing essentially the same things as the brains of less intelligent mammals, like mice, except at a larger scale?

How simple is the simplest brain that can be scaled?

If mammalian brains can be scaled, what's the simplest brain that could? A turtle's? A spider's?

How close are we to simple biological brains?

Given how little we understand about how brains work, do we have any reason to think we can recapitulate the algorithmic function of even simple biological brains?

What's the smallest set of principles that can explain human cognition?

Is there a small set of principles that underlies the breadth of cognitive processes we've observed (e.g. language, perception, memory, attention, and reasoning)[2], similarly to how Newton’s laws of motion underlie a breadth of seemingly-disparate physical phenomena? Or is our cognition more like a big mess of irreducible complexity?

How well can humans compete against evolution in designing general intelligences?

Humans can design some things much better than evolution (like rockets), and evolution can design some things much better than humans (like immune systems). Where does general intelligence lie on this spectrum?

Tying it all together, part I

My framework for what these variables tell us about AGI timelines

Tying it all together, part II

My personal views on AGI timelines

  1. https://en.wikipedia.org/wiki/Neocortex ↩︎

  2. https://en.wikipedia.org/wiki/Cognitive_science ↩︎


The Great Annealing

30 марта, 2020 - 04:08
Published on March 30, 2020 1:08 AM GMT

Cross-posted as always from Putanumonit.

Michael Johnson of the Qualia Research Institute wrote an overview of the theory of neural annealing.

The metaphor comes from metalsmithing. A newly forged sword is ductile and tough. Then you bash the outgroup on the head with it and every bash creates a stress point in the sword: some part is bent, or stretched, or slightly cracked. Even elastic metal can’t spring back perfectly to it’s original shape, and the sword becomes brittle and stiff. To restore it, you heat the metal to the point where its microstructure breaks down, then let it slowly cool and crystallize into stable unstressed patterns again.

Johnson, building on the work of Robin Carhart-Harris and others, proposes that the same process applies to brains. In the predictive processing paradigm, the brain starts a cycle from a stable state: neural activity is low-entropy and proceeds in fixed patterns. From the inside, this feels like confidence in your models of the world, and consistent affect in your reaction to things.

As more input comes in, the brain accumulates stress in the form of prediction errors. This manifests as confusion, anxiety, or unease: a person who reacted unexpectedly to something you said, news that upset you, an action that failed to achieve it’s intended consequences.

An ideal Bayesian brain would immediately update all of its model in light of every new bit of information, but our brains cannot physiologically do that. The stress of prediction error accumulates in the form of electrical energy in particular neural circuits, but can’t propagate everywhere (or even reach consciousness) until it crosses a particular threshold of excitation. Your conscious and unconscious models of reality live in silos with their own accumulated errors, separated from the rest of the brain by potential energy barriers (Scott Alexander calls them mental mountains).

To update, your brain needs to heat up, to build up too much energy at once to route through the habitual circuits of thought. Meditation and psychedelics are powerful tools for achieving that, but anything that fills your brain with new and unusual inputs can do the trick like a retreat into the woods or “mindblowing” sex. From the inside, a brain pumped full of entropy feels like a trip: unusual and novel thoughts without strong affect, creative excitement, or nothing at all if your mind is too chaotic to make any sense of it at all.

Once past a critical point, the mental mountains flatten out and all the modules of the brain can “talk to each other” and update at once. They can settle into a new equilibrium that would globally minimize the prediction errors accumulated so far and those expected in the future. This feels like insight or enlightenment, depending on the magnitude of the update.

Curing Jealousy

That was the theory, but I’ve had some powerful first hand experience with the process as well.

When I first began experimenting with open relationships, I didn’t think I was someone particularly immune to romantic or sexual jealousy. My partner at the time and I adopted a don’t-ask-don’t-tell policy. We treated jealousy as something to be circumvented, an ultimately useful emotion that needs to be suppressed in some novel contexts, similar to anger or fear. Even famous polyamory advocates like Geoffrey Miller carefully tiptoe around jealousy and distract themselves with utilitarianism and TV.

But as I read more books about evolutionary psychology, especially those by Geoffrey Miller himself, I began to question the utility of jealousy altogether in my life. Jealousy served the genes of my ancestors who needed to secure reproductive and resource exclusivity, considerations that aren’t relevant in 2020 and for my actual well-being.

The two models of jealousy inhabited my brain simultaneously — the evolved instinct to feel it, and the conscious idea that I’d be happier without it. Both models were accumulating error signals: the latter every time my wife went out on a date and I felt anxious and jealous, the former every time she came back happy to see me again and not pregnant. I lived with this internal conflict for the seven years since my first open relationship.

And then, I went on a retreat in the woods with friends. I meditated, had sex, and took psychedelics. With my brain as full of entropy as it’s ever been, I saw my wife cuddling with a male friend, both of them smiling at me. And I realized that the usual pang of jealousy that I expected to feel was completely gone, released like a muscle knot under massage.

I tried to summon the pro- and anti-jealousy parts of mind to hash their differences out in an internal double crux, but I was past that point already. Only the unjealous part showed up. I spent seven years contemplating jealousy and seven hours neural annealing, and by the end of it the jealousy was gone and remained gone.

Societal Annealing

It’s an irresistible temptation for anyone writing about neural annealing to apply the same model to society, particularly those coming from the view of an individual brain as comprising a society of multiple agents. Johnson writes about “social annealing” that happens in a shared context of religious service or sporting event. Carhart-Harris and Friston talk about psychedelics destabilizing social order and discomfiting the ruling elite in the same way they shake up the dominant thought patterns ruling the rigid brain.

The model, broadly speaking, is the same. Individuals in a society have their own worldviews and theories of reality, making their own individual predictions (implicit or explicit) and accumulating successes and errors. But in the normal course of things, good ideas don’t propagate and bad ideas aren’t dislodged very rapidly.

The world runs its course, people go to work and drink beer and watch TV regardless of the wrongness of prevailing ideologies. Almost everyone’s lives is governed not by physical reality but by the social reality around them, and unlike physical reality which is one, there can be as many coexisting social realities as social groups willing to courteously ignore each other.

In our brains, conscious awareness is the central stage on which mental agents can broadcast their models to the mind at large. Communication between mental modules occurs outside of awareness as well, but in a much more limited capacity. In society, the spread and proliferation of ideas is controlled and gated by central sensemaking institutions: the government and mass media. In a world not constrained by physical reality, these institutions can spin narratives full of errors and contradictions that go unnoticed.

But once in a while, physical reality grabs the world by the shoulders and gives it a hard shake.

In social-reality times, government can make bad policy, schools can teach bad ideas, newspapers can print nonsense, and they would not be found out for many years if at all. In an exponentially growing pandemic, bad policy and fake news get found out within days if not hours. Ideas that held center stage through sheer inertia and suppression get swept away by the tide, and smart ideas that were locked in a dark corner can spread and propagate at the speed of Twitter.


No matter what happens in the news, a predictable consequence is political ideologists of all stripes will claim that the latest thing is proof of their wisdom courage and a complete refutation of their opponents. Progressives and conservatives, statists and liberatarians, are equally convinced that COVID validates their ideology and supports their pet policy prescriptions.

But I think that more and more people are realizing that political ideology has very little to do with how the state is governed. Political parties are merely coalitions of convenience, and the response of elected officials has nothing to do with their team colors and everything to do with their personality and circumstance.

A Republican president just signed America’s largest ever welfare bill, approved by a Republican senate. Their Democrat colleagues supported the travel restrictions and border closures. The governor of New York is deservedly getting praise for his decisive and informed action while the governor of Nevada is deservedly getting shit for banning doctors from prescribing hydroxychloroquine — they are both “moderate Democrats”, and that fact has zero bearing on their policy decisions.

The same is true on a global scale. Taiwan’s response has been exemplary and Spain’s abysmal; they are both democracies. Vietnam’s single party has managed to contain the disease so far while Iran’s single party is digging mass graves. Elected and unelected rulers are responding to the virus according to their whims, personal interests, whatever advice they may or may not be receiving, and circumstances that are mostly outside their control.

I hope that this will mark a reversal in partisan polarization, and a marginalization of partisans who continue to twist everything into their pet causes. Inshallah we shall see an abatement of the culture wars as the nature of the crisis beclowns identity politics on both the right and the left. But some people have been making a living stoking the culture war flames, and I don’t think they’re going to adjust well to a world of physical reality.


While political ideology seems to matter little in how governments respond to a real crisis, the response by many media outlets showed that they contain little else.

Opinions of a given media outlet usually fall into two camps:

  1. They are good people who tell the truth.
  2. They are bad people who lie for personal gain.

This was my default implicit model  — I thought of the truth I would print if I was a journalist, and attributed the falsehoods I saw in print to dishonesty.

In early February, when “prestige media” were churning out daily misinformation about the virus, Mason Hartman attributed their behavior to camp #2. I noticed that I was confused: wouldn’t the personal gain come to the alarmists who get lucky? We remember the people who predicted the 2008 financial crisis that happened; we don’t remember who predicted the crises of 2010, 2011, 2012 etc that didn’t.

But then author Tucker Max responded:

At first, I thought this was just snark, or a too-enthusiastic application of Hanlon’s Razor. I know a few journalists, and they are intelligent and curious people. What are the chances that I just happened to meet the outliers of the profession?

But the journalists kept writing, and Tucker’s tweet loomed larger with every article. Here’s a selection from just one outlet which doubles as the Rationalist’s official outgroup, Vox Media:

It won’t be a pandemic, the flu is worse, travel bans don’t work, masks don’t work, keep shaking hands, only racists are scared — all of these were reckless on the day they were written and proven false within mere days of publication. Nobody who is bending the truth for personal gain would write these articles. They were written by people who don’t know what truth is, who lost sight of the fact that reality exists and will pass judgment on their articles based on physical outcomes and not pure intentions.

They were written by people who don’t have the intelligence and scientific literacy to comprehend the mathematics of exponential growth, the physics of droplets in the air and on fabric, the difference between a study failing to show significance for an intervention because of a small sample size and a study “disproving” its effectiveness. These “explainer” articles are simply pattern-matching whatever news was printed elsewhere into a small set of affect-laden narrative templates: orange man – bad, big government – good, Silicon Valley – bad. (Most major media outlets have stumbled onto at least a few people who start from truth-seeking and not from narrative matching. I wonder how these writers are feeling about their “colleagues”.)

Most of these articles are neither truth nor lies, they have practically no information content aside from a few cherry-picked numbers floating free of context. You cannot learn anything by either taking Vox at face value or by flipping the affective sign on what they write. You cannot reverse stupidity to produce intelligence, which is another thing that a Vox correspondent is too stupid to understand.

Novel Sensemaking

Coronavirus has broken sensemaking for a large chunk of society. Some people interpreted events outside their immediate lives through a lens of partisan ideology, but partisan ideology turned out to be little relevant to the crisis. Some people relied on explainer media, but explainer media turned out to do little more than dress up partisan ideology in prettier language. Some people ignored world events completely, but now world events are making their way into your city, your job, into your lungs.

And this is the blessing of the COVID disruption — it’s an opportunity to learn new ways of making sense of the world. We should do it the same way our brains do it, by seeing which models make correct predictions and which ones go wrong. The novelty of the situation and the speed of unfolding events have tightened the feedback loops between our beliefs and reality, and the internet allows us to update together without relying on central authorities.

This doesn’t apply just to the virus itself. People are unlearning old ways of looking at education (was it just daycare all along?), the markets (aren’t they supposed to be efficient?), and the actual economy (why can the US make space rockets but not paper masks?) People are learning new things about their families, friends, roommates, and landlords.

If you’re feeling anxious and overwhelmed, that is a sign that the annealing has started. Old useless patterns have broken, entropy has replaced the semblance of order. But don’t hide away from the confusion, use it to build new ways of understanding, alone and with people you trust.

Make predictions and track them, and make a note of when you were right and wrong. Track the implicit predictions made by others, and whether they admit their mistakes or refuse to update. Make beliefs pay rent.

Give the absurdity heuristic a rest — the world is absurd. Downgrade credentials and titles, they are indicators of social reality. Recognize people who show tangible results, those come from physical reality.

And finally: meditate, have sex, work out, do drugs, whatever is available to you. Don’t escape completely into Netflix and video games, artificial worlds designed by clever writers to be easily made sense of in predictable ways. Absorb information and let your brain anneal, then spread the word to others. The world is not any stranger today than it was a month ago. Since the beginning not one unusual thing has ever happened. It is only our minds that fell behind. Now is the time to catch up.


C19 Prediction Survey Thread

30 марта, 2020 - 03:53
Published on March 30, 2020 12:53 AM GMT

I'm curious about people's current predictions of future:

  • US confirmed case count
  • US death count

As a schelling point, let's use May 1 (5/01/2020) as the prediction date, and worldometer as the data source.

Predictions should be in the form of a simple distribution such as a normal or lognormal.

My current predictions:

US cases: lognormal(13.0, 0.3) mode: 404k

US deaths: lognormal(10.0, 0.6) mode: 15.3k


Would Covid19 patients benefit from blood transfusions from people who have recovered?

30 марта, 2020 - 01:27
Published on March 29, 2020 10:27 PM GMT

Epistemic status: wild idea thrown out there in case it is brilliant.

Presumably, the blood of recovered patients has some antibody that targets the virus. Blood transfusions are widely understood and known safe. (Or at least safe so long as you don't do a few things that doctors know not to do, like giving AB blood to an O patient. ) Is it another thing that should work in principle, is more likely than not to work in practice, but by the time someone has run the medical trial, it will be all over. Of course, the recovered patients blood contains enough antibodys to make them immune, but I don't know how big an effect a fraction of the concentration would have. Of course given exponential growth rates, there might be far more critically ill patients than recovered patients. It might be possible to separate the antibody from the rest of the blood, or even grow it by infecting cells with covid19 in a lab.


Approaches to Electronic Contra

30 марта, 2020 - 01:20
Published on March 29, 2020 10:20 PM GMT

I've been thinking about the various ways that musicians have approached combining elements of electronic dance music with contra dance. Examples:

This is not at all a complete list, but I think it shows the range of what people have tried.

One axis of variation here is how much of the playing is live. At one end you're dancing to an entirely premixed track, while at the other all the sounds are triggered in the moment by the musicians:

  • Fully pre-mixed: Lisa Greenleaf, Emily Rush (Rushfest), and several other callers come to the dance with fully mixed-down recordings. Some "DJ"s are here, doing all their composition ahead of time.

  • Mostly pre-mixed: DJ Improper and some other contra DJs prepare their sets in advance, but can remix them some on the fly. This is what a live DJ traditionally does.

  • Hybrid: Julie Vallimont's various bands [1], Phase X, and D. R. Shadow combine live elements (fiddle, piano, jawharp, accordion, sax, banjo) with loops, beats, samples, and mashups.

  • Live looping: Perpetual e-Motion would come in with nothing pre-recorded, and would build up a complex texture with loops.

  • Fully live: this is mostly not a thing, though this is close. It's very hard to make the full sound of a modern electronic dance track with a reasonable number of live musicians.

Another axis of variation is originality, in the sense of "from scratch". At one end you have music that is based around commercial recordings, with edits, remixes and mashups, while at the other end you have musicians writing their own music. This is separate from the previous axis: you can make pre-mixed tracks from club hits or your own compositions; you can play faithful pop covers live or figure out your own arrangements.

Even though it's separate, they do tend to go together: people who work pre-mixed are mostly remixing and editing recordings, while people who work hybrid, looped, or fully live are mostly making their own thing. I've been thinking some about why. Part of this is that the easiest way to get started is to find existing recordings that support the structure of the dance, or make small edits to them to get it to fit. The tradeoff, though, is that you have much less control. You can very occasionally get multitrack versions ("stems") of songs, but usually you have a fully-mixed track. If you pick tracks you like this will sound good, but you're at a bit of a musical local maximum.

Over time, as people get more experience, they tend to move farther along the "originality" axis. Comparing Vallimont's work in Double Apex (2010-2012) vs Buddy System (2014-), or Jacoby's in Phase X (2012-2013) vs Ground Lift (2019-), earlier work involved a lot of mashups but they've both moved on to a "fully original" approach.

With Kingfisher and my rhythm stage setup I've been exploring the edges of this from a live direction. I want to keep the full control and ability to improvise that I have on the mandolin or piano, but with the full sound of a large band or modern produced recording. (For example, here's something I was working on yesterday which is fully live and uses only my feet and breath.) Looking over what parts of the space have been explored or not and how that has gone is useful for figuring out what things I want to try.

[1] Double Apex with Brendan Carey Block, Firecloud with Andy Reiner, Delta Wave with Jon Cannon, Cosmic Echo with Ed Howe, and Buddy System with Noah VanNorstrand.

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How to Make Money From Coronavirus

30 марта, 2020 - 00:24
Published on March 29, 2020 9:24 PM GMT

Crossposted with delay, lightly edited from original

Disclaimer: There is no substitute for personalised financial advice; be wary of helpful tips from strangers on the Internet.

tl;dr - makes the case for conventional investing wisdom. Posting as the boring counterpoint to some of the clever advice floating around, which is perfect bait for rationalists.

THIS MIGHT SOUND GAUCHE while people are dying in horrifying ways, but people are constantly dying in horrifying ways, and besides, I know you’re thinking about it.

Apocalpyse scenarios crank up the dial on all three of the human emotions: fear, hunger, and horniness.

I already ate all my emergency snacks by day two of quarantine and this is a family-friendly blog, so let’s stick with ‘fear’.

It’s hard not to be scared about your financial future, especially if you a) just lost your income source, or b) are currently missing tens of thousands of dollars from your investment account.

I can’t do much about the first one. The stretch goal of this post is to get those dollars back in your investment account, and then a whole lot more besides. The modest goal is to prevent anyone from accidentally blowing their own foot off.

‘Managing to avoid shooting yourself in the foot’ is not hugely inspiring as far as aspirations go, but I’m convinced it’s the secret to winning in life.[1]

There are a couple of cheap/free options you can to take out to limit the near-bottomless downside risk of COVID-19. I mentioned them in The Inevitable Coronavirus Post but they’re time-sensitive; hopefully you took those steps before the panic-buying and lockdowns began.

Now it’s time to think about the upside. When a black swan flaps its wings, great risks and opportunities swirl out of the same chaos.

What would it take to not just weather this situation, but profit from it?

Taking the question broadly: there’s a bunch of status up for grabs. A few online communities and individuals who were front-running this thing have seen big increases in their audience and platforms, and deservedly so—they profited from the willingness to be weird in the face of intense social pressure to sit patiently while the room fills with smoke. Meanwhile, the incompetence of authority figures and institutions has revealed that there are no grown-ups in the room. These dynamics are fascinating but not exactly Actionable Content, so let’s leave that for another time.

In the narrow financial sense: I think it’s possible to make some serious money from this thing.

The first thing to note is that the bar has been set unusually low. Normally, when stocks tank, other assets go up, meaning there are plenty of winners crowing about their prescient investing decisions.

The incredible thing about this crisis is that hardly anyone is winning. Bonds are falling. Gold is falling. Bitcoin is falling, even as the money printers go brrrr. Even frickin’ Treasuries no longer look like a safe haven.

In short, every liquid asset is getting punished. People are selling everything up to and including the kitchen sink, stripping the copper wiring out of the walls, breaking the house down for scrap, then auctioning off the ground beneath it, etc.

This unprecedented liquidity squeeze is mostly just a curiosity for finance wonks and economists to puzzle over (feel free to enlighten me in the comments). For our purposes, it means we don’t have to worry so much about how the Joneses are doing. No-one’s making bank right now; not even that guy who tried to price-gouge 18,000 bottles of hand sanitizer.

The second thing to note is that some investors are pretty much screwed. There is no sugar-coating this, so we might as well get it out of the way first.

Don’t Make Plans While Being Punched in the Face

Everyone has a plan until they get punched in the face.


This is a great quote but it’s nowhere near pessimistic enough: hardly anyone has a plan to begin with!

Are contingency plans tough to stick to? Yes. But having a plan in advance is still much better than trying to come up with one while being repeatedly punched in the face. You’re in fight-or-flight mode, your brain is bouncing around your skull like a pinball, your amygdala is stewing in a marinade of fear and adrenaline.

Looking back at this moment many years from now, how sound do you think these decisions will be?

Instead of trying to make life-changing decisions in the midst of market mayhem, you should just stick to the plan you prepared at some point during the last 10 years of good times. Which is totally a thing that everyone did. Right?



*wheezing segues into panic attack*

OK. Excuse me; this is therapy for me. Here’s the relevant quote from If the Market Crashed Today:

When the crash comes, can you brave a 30 per cent decline without flinching? What if it happens in the space of a couple of days? How will you sleep at night? Do you have an ulcer? No? Do you want one?

I think it’s a good idea to visualise these kinds of scenarios in as much detail as you can, and really try to ‘feel’ it. Be honest with yourself about how you might respond in a crisis. Maybe it makes sense to take some money off the table, or have a portfolio that isn’t 100 per cent in stocks, even if it’s technically not the best strategy.

If you didn’t take this advice, well… you really should have taken this advice, but that’s OK. All is not lost!

That is, assuming you don’t need your money out any time soon:

Less than five years: what the heck are you doing in the sharemarket? Less than 10 years: still a bit iffy.

For those who ignored this piece of advice, repeated ad nauseum in every investing 101 article since the dawn of time, there is no ‘good’ strategy for getting out of this with your hide in one piece. Sorry.

Anyone who needs their money out in less than 10 years is now in the unenviable position of trying to time the market.

Timing the Market

...that is the question

As at the time of writing, the S&P 500 is down 30 per cent from recent highs. To put it in context, that’s three year’s worth of gains wiped out—a big deal, but not disastrous in the context of a decade-long bull run.

If you sell out, you’re locking in that loss. Maybe this is as bad as it gets, in which case it’s better to ride it out and catch the rebound.

On the other hand, maybe there’s a whole lot more pain to come: markets tanked 56 percent during the last crash, and 86 per cent during the Great Depression.

So, what to do? How long will it take to come out the other side? Is this a quick in-and-out adventure? A V-shaped recession? A long, grinding, decade-long depression?

I don’t know, and anyone who claims to know for sure is lying or deluded.

This is the most important sentence I will write, so read it again: I don’t know how long this will take, and anyone who claims to know for sure is lying or deluded.

Now, a few lucky souls will make their exit with perfect timing, and get back in near the very bottom. Doesn’t that mean they outsmarted the market?

Remember that you’re far more likely to hear from these people, who are under incredible internal pressure to believe they are brilliant gurus, than everyone else who tries to be clever and gets the spanking they deserve. This is survivorship bias, and it’s just one of many cognitive pitfalls which makes investing a deadly honey trap for smart people.

If you are smart, you might think you can use your big juicy brain to quickly become above-average in many areas of life. This is occasionally even true! But oh man… not when it comes to investing.[2] In public markets, even the smartest people are like ants, trying to outsmart the closest thing to a superintelligence humankind has ever created.

(I’ve previously written a bunch about passive investing, trying to catch a falling knife etc, so I’m not going to relitigate this here.)

Timing the market is risky business. You have to get lucky twice: on the way out, and on the way back in. If you have no choice but to try anyway—maybe you need to preserve your capital for a house deposit, near-term spending, or an unexpected emergency—I sincerely wish you all the best. It’s a crappy situation to be in.

Everyone else can breathe easy. When the streets are running red, it’s those who don’t try to time the market who reliably make big bucks.[3]

How big are we talking? I think perhaps quite a bit bigger than many people would expect. Let’s have a look.

The ‘Big Brass Balls’ Portfolio

I was too shy to get a picture, but I enjoyed rubbing the enormous bronze testicles of the Charging Bull when I visited NYC, as is the local custom, while thinking about getting rich (the other local custom)

During the global financial crisis, the stock market was cut in half. It took ~65 long months to get from the pre-GFC peak to the eventual recovery. What happened to those brave souls who kept faithfully investing throughout the chaos?

Let’s call the stocks bought during this period the Big Brass Balls portfolio, or BBB for short.

You start accumulating in November of 2007, while the world is on fire and everyone is freaking out. On the first day of each month, you buy more stocks, and reinvest any dividends.

To begin with, your strategy looks terrible. Almost every month, your returns drop further into the red. After a year and a half, things have never looked so bad:

Then the recovery begins. By April 2013, the S&P 500 has finally wound its way back to where it was pre-GFC. Womp womp.

While the headline index has moved 0 per cent in almost six years, that’s a deceptive indicator. It doesn’t include the impact of reinvested dividends, or the fact that you’ve been consistently buying in at discounted prices. And so, while most people have made hardly anything in this period, your BBB portfolio has appreciated by 42 per cent:

This is already pretty great, but the real payoff is yet to come. One year later, your BBB stocks are up 73 per cent. Now, they’re starting to seem like a bargain.

Three years later, they’ve almost doubled in value. Now they seem like a fantastic bargain:

How about right now?

It feels like the world is teetering on the brink of collapse, but those stocks are still up…155 per cent.

Let’s zoom out once more, just for fun, and put the current crash in its proper context:

The BBB portfolio generated an excellent return through the previous crisis, even though it uses the simplest possible strategy.

There is a very good reason why ‘buy and hold forever’ is the gold standard in investing advice. It requires zero attempts at timing the market, zero clever ‘insights’, zero knowledge, zero luck, and if you do it right, zero self-discipline.

Four Possible Failure Modes

How might the classic buy-and-hold advice fail us? I can think of at least four ways:

1. Losing Your Nerve

This is my first proper crash, so I don’t know how psychologically demanding it is to keep buying the dip. Early signs suggest I’m as yellow-bellied as anyone, but I’m confident I will ultimately take my own advice. For many seasoned investors, who have already been through this three times, this is old hat.

Can we protect against this failure mode?

Yes! Set up an automatic direct debit to your broker, ban yourself from ever looking at your account, and go about your life as normal. Then you can take all the anxiety and fruitless efforts to time the market and use it to fuel more interesting neuroses, like the prospect of a superintelligent AI converting the universe into paperclips.

2. Crying ‘Uncle!’

Even if you know you have the intestinal fortitude required to see this through, the universe has a way of messing with best-laid plans. From If the Market Crashed Today:

Traders talk about their ‘uncle points’. When someone is twisting your arm out of its socket, there will come a point you’re in so much pain that you have no choice but to cry ‘Uncle!’ and close out the position, no matter how much it humiliates you, or ruins all your plans.

Maybe you don’t plan to cash out your investments any time soon. But if you lose your job, or your marriage, or your house burns down, or a loved one gets sick, you might have to sell with the worst possible timing.

Can we protect against this failure mode?

Yes, but not perfectly. Uncle points can be eliminated by buying insurance, maintaining emergency funds, and generally having a plan for various unthinkable scenarios. There's always some level of uncertainty, but you can reduce the chances of getting your arm twisted at an inopportune time.

3. Lack of Surplus Income

Lots of people are in for a tough time; unemployment is going through the roof. You can’t keep buying stocks if you don’t have a reliable source of surplus income.

Can we protect against this failure mode?

This is not really a ‘failure’, so much as a limitation in who can take part. If you’re nearing retirement, I guess you could consider working a little longer? I haven’t had a proper job in a while, but I’ll probably start doing a little more (paid) work for this same reason.

4. This Time is Different

The buy-and-hold-forever strategy does rely on one ‘insight’: the observation that humans steamroll over absolutely anything that comes between us and our incredible thirst for wealth creation: be it the Spanish Flu, two World Wars, the Great Depression, 9/11, the implosion of the financial system, whatever.

I am going to go out on a limb and predict that COVID-19 will one day join this list.

Could this time really be different?

It’s always possible!

Past performance doesn’t predict future returns, and the world is only ever getting weirder. If global stock markets don’t recover in our lifetimes, something has gone so terribly wrong that the balance of our retirement portfolios is probably not gonna be top of mind, but it’s still an interesting question.

If the current situation has taught us anything, is that it’s entirely rational to be paranoid about vanishingly-unlikely tail risk events.

I tried to model these kind of edge cases in Beware of Geeks Bearing Formulas. The general takeaway is that the year in which you happen to start drawing down your portfolio has a huge impact on your fortunes (think of someone who blissfully celebrated their retirement in 2019, and is now burning capital with the worst possible timing), but the window of history during which you’re accumulating doesn’t matter nearly as much.

This is one of several reasons I’m no longer keen on the whole ‘early retirement’ thing. It works out great for many people, but it’s a bit riskier than it appears.

Can we protect against this failure mode?

Kind of? The safest possible strategy—financially, and for life satisfaction—is to find work you enjoy, and do it forever. If that’s not possible, well, going long on human innovation and wealth creation has never failed us before. Hopefully this time is no different.

The Irresistible Appeal of Being Clever

OK. I put a very sexy headline on a very boring strategy that everyone already knows about. Why bother? Because, incredibly, this boring strategy that everyone knows about is still low-hanging fruit!

Consider that plenty of very smart people are incapable of following this very simple and obvious strategy; that the best investors are literal cadavers who can't trade their accounts; that the person who is telling you all this is a gigantic hypocrite who struggles to take his own advice.

I’ve been corrupted by working adjacent to the finance industry: a little knowledge is a dangerous thing. If you don’t know anything, and are worried about missing out on all these clever strategies you’ve heard your smart friends talking about, I give you permission to stop worrying.

This is one of those bizarre opportunities where carefully stewarding your ignorance and being as lazy as possible is almost certainly the best approach. It’s as if God made broccoli taste like chocolate; or we could defeat the greatest threat to human life in a generation by… staying home and sitting on the couch.

So, that’s the opportunity in front of us. The next few years will separate the boys from the men, the girls from the women, and the non-binary from the non-binary.

I promise to revisit this post in March 2025, so we can see whether it stands the test of time. If it holds up, and I manage to stop being clever long enough to actually follow my own advice, I will immediately become insufferably smug, and start branding myself as a brilliant investing guru.

If it doesn’t hold up, and you’re mad, well. Come find me and my reaver gang in the badlands of Mexico, and say it to my (steampunk apocalypse masked) face.

a real ad that amazon keeps showing me

  1. In optionality terms: the most important thing is always to stay the hell away from anything that might turn out to be a Bottomless Pit of Doom. ↩︎

  2. This problem gets even worse among the kind of smart people who know a lot about cognitive biases, because they think they’ve compensated for them, and end up even more overconfident. I'm guilty of this too, but I don't pretend there's anything 'rational' about it. IMO a true rationalist would spend most of her time acting upon the Boring Advice Repository. ↩︎

  3. This might be a good time to confess that I’m currently testing a momentum (trend-following) strategy with a small part of my portfolio, which, uh… flies in the face of all of the above. It’s performed almost comically badly through the COVID-19 crash, which ought to have been a delicious punishment for my hypocrisy and hubris, except that I just so happened to over-rule the crucial decision (i.e, I got lucky). I’m also tracking the counterfactual where I stuck with the experiment: if momentum really does turn out to be a persistent anomaly in otherwise efficient markets, then that would be fascinating—either way, I’ll try to write up a review some time later this year. ↩︎


Global Online Zoom Meetup - April 5th, 10:30 PDT

29 марта, 2020 - 21:39
Published on March 29, 2020 6:39 PM GMT

I attended a Zoom meetup last week hosted by LessWrong Israel.

We had over 60 people join up and it was a lot of fun. The session worked like this: Volunteers took 4 minutes discussing or explaining a non-COVID-19 topic of their choice. Subjects included the strange history of a Japanese rice market, an ambitious attempt to bring back the Charleston after a century-long quiescence, a lesson on how to be a good DnD player, and an explanation of a counterintuitive recreational maths problem involving the probability of rolling "666". (The Kabbalistic implications of this are left as an exercise for the reader.)

Then after time was up we voted, and if the crowd voted "yes" the speaker got another 3 minutes to continue their talk. After 90 minutes, we then ended the session but anyone who felt like hanging out and talking was welcome to.

If after reading this you are sad you didn't participate, then you are in luck. Provided I get enough interest, I will be hosting a similar meetup on Sunday April 5th at 10:30 PDT.

Link to register.

Zoom details here:

Topic: SSC/LessWrong Meetup
Time: Apr 5, 2020 10:30 AM Pacific Time (US and Canada)
Zoom Link


COVID transmission by talking (& singing)

29 марта, 2020 - 21:26
Published on March 29, 2020 6:26 PM GMT

TL;DR: This is an argument that (1) Talking is a major way that COVID-19 transmits from person to person (at least when people aren't wearing facemasks and goggles, and possibly to a much lesser extent even if they are), right up there with coughing, and probably more important than touching contaminated surfaces; (2) The public is not generally aware of this, at least where I am; plus (3) Sketchy ideas for what to do about it.

Epistemic status: Low confidence, open to criticism.

Background: Why think that talking (at least without facemasks) is a major transmission risk?
  • This NIH video shows how talking creates droplets and aerosols, just like coughing does, and strongly advocates that everyone should avoid face-to-face conversations. They also made a follow-up video showing that facemasks help, even the kind of DIY facemask made from old T-shirts.

  • This CDC webpage emphasizes respiratory droplets and avoiding close proximity, adding that "contaminated surfaces or objects … [are] not thought to be the main way the virus spreads." The Chinese government guide concurs.

  • This article by a domain expert argues that the main way people catch it is probably if someone is talking or coughing nearby, and agrees with the CDC that touching contaminated surfaces is probably less of a big deal, albeit with considerable uncertainty from the lack of data.

    • (She gets really pedantic in arguing that we should use the terminology "short-range aerosols" for what's going on, while the CDC, WHO, and everyone else seems to call the same thing "inhaled droplets". I really don't care about the terminology. Everyone agrees about what's going on. But to be clear, the term "aerosol" usually refers to long-range aerosol transmission, like measles, and there seems to be a consensus that this type of traditional long-range aerosol transmission is not a major part of COVID-19 transmission, for reasons discussed here, including observations about who catches it from whom.
  • As jimrandomh and others have argued, there is a lot of asymptomatic transmission—so it’s not just people coughing.

Super-spreader events seem to involve singing indoors and/or face-to-face conversations

Two known COVID-19 super-spreader events may have involved the infected person singing indoors for an extended period:

First, the Skagit Valley Chorale case:

...The virus has devastated the Skagit Valley Chorale, based in the rural valley north of Seattle... Of the 60 people who attended a March 10 practice, 45 have developed symptoms and 27 so far have tested positive, officials said. One of the group’s members has died, another has been hospitalized and others have struggled to overcome their illness.

Ruth Backlund, a co-president at the Skagit Valley Chorale, said the group was monitoring public health guidelines at the time of the practice and had asked people to stay home if they showed even minor signs of illness. The group gathered in rows facing a piano and a choir director. They were all in individual chairs and had space to keep separated. Ms. Backlund had made sure there were extra soap dispensers in the bathrooms for people to wash their hands.

"Nobody was sick. Nobody touched anybody. Nobody shook hands. Nobody hugged everybody like you might do in a group. There was none of that," Ms. Backlund said.

Second, maybe the case of the Shincheonji religious sect in South Korea. I don't know the details, but in general, "Attendants remain seated on their knees throughout the service, chanting Amen and singing en mass".

Other super-spreader events seem to be consistent with face-to-face conversations with asymptomatic individuals:

The Connecticut party presumably involved face-to-face conversations, though it seems that few details have been published. The infected person is said to have been asymptomatic.

The Boston Biogen Conference involved people milling around with hors d’oeuvres and drinks—again, presumably consistent with spread by face-to-face conversation. It seems that nobody was coughing there, though I could be wrong.

What can we do?

I think public health messaging has not gotten through about this. I propose a motto / meme:

When someone talks to you, they're spitting on your face!

This message would help people understand viscerally why they're being asked to stay 2 meters apart. It would also help people understand viscerally why people are telling them to make and wear DIY facemasks & goggles. (I just made myself a mask yesterday!)

Beyond that, awareness by itself would be really valuable in directing people's decisions and actions. For example, where I live (USA), many of my local businesses (grocery stores, take-out restaurants, etc.) are going above and beyond in frequently sanitizing surfaces, non-contact e-payments, staff washing hands and wearing clean gloves, etc. etc. ... yet shopping there still involves a face-to-face conversation with the salesperson (who does not have a mask)!!

I want every boss of every essential business to tell their employees: Don't try to start a face-to-face conversation with your coworkers or customers (especially without facemasks), it's just as rude and reckless as coughing on them, or pushing them into a busy street. I want a cultural norm that says that if someone tries to start a face-to-face conversation, the other person is entitled to run away!

Again, I could be wrong altogether ... and if this is a message worth promoting, I'm not sure how to get lots of celebrities etc. to say "When someone talks to you, they're spitting on your face!", instead of "wash your hands" (which is still a good idea but everyone has already heard that a million times).


Peter's COVID Consolidated Brief for 29 March

29 марта, 2020 - 20:07
Published on March 29, 2020 5:07 PM GMT

COVID-19 is a rapidly changing situation and it is hard to keep up to date. However, right now I am following COVID-19 full time and I read widely and I read a lot. I’m going to experiment with providing a public consolidated brief that tries to consolidate everything I read into one short, actionable list so other people don’t have to re-create my work. This way I can save time and fight research debt.

This brief assumes you are up to date on most things that have happened since around the 25th of March and will aim to keep you up to date on the latest over the past three days or so.

Note that I am not a domain expert and I urge some caution in over-relying on my selection and interpretation of these links.

This brief follows my research agenda. I am going to keep that up to date as well. I will also keep eyes on the LessWrong Coronavirus Agenda and submit to the LessWrong links database. Further discussion will be in the EA Coronavirus Facebook Group.

Doing Your Part! How You Can Stay Safe and Help the Fight!

Rob Besinger offers some advice for staying safe that I have not had the time or expertise to verify, but will reprint uncritically:

  1. definitely, definitely self-quarantine
  2. Avoid people
  3. If you do need to be around people, wear something over your mouth and nose
  4. Don’t touch your face (duh)
  5. Wash your hands (duh)
  6. Eat well, sleep well, get exercise
  7. Consider stockpiling a month of food (...if you still can at this point, IMO good to have a at least a week or so above your usual amount of food)
  8. Consider printing out copies of your health records
  9. Regularly disinfect commonly touched surfaces like door handles and light switches
  10. Consider covering commonly touched surfaces with copper tape
  11. Probably stop taking NSAIDs like ibuprofen
  12. Probably even-better-advice-than-normal to consume 2000-6000 IU of Vitamin D daily, in the morning
  13. Consider running an air purifier
  14. Understand how COVID-19 usually presents and progresses, so you can make an informed guess about how likely you are to have it
  15. Take zinc immediately if you start feeling any cold-, flu-, or COVID-19-like symptoms
  16. Start monitoring your oxygen immediately if you develop a fever or experience significant chest tightness or difficulty breathing


80,000 Hours puts out a list of things people can do to help with COVID:

  1. Research to understand the disease and to develop new treatments and a vaccine.
  2. Determine the right policies, both for public health and the economic response.
  3. Increase healthcare capacity, especially for testing, ventilators, personal protective equipment, and critical care.
  4. Slow the spread through testing and isolating cases, as well as mass advocacy to promote social distancing and other key behaviours, buying us more time to do the above.
  5. We also need to keep society functioning through the progression of the pandemic.

80,000 Hours thinks it is people should switch to working on COVID-related projects if they’re roughly in the top 4% of people best suited to work on it - typically people who:

  1. have highly relevant skills and/or useful connections - especially those who have medical training, can help with urgent hardware or software engineering efforts, or have knowledge of vaccines, public health, and government institutions
  2. are not otherwise doing really important work
  3. are highly motivated and informed on COVID
  4. can switch into COVID-related work and switch back after without derailing one’s long-term career


Here’s my personal list of things you can do:

  • Find your equilibrium, prepare yourself, and make sure you are okay first before trying to help. Make sure you have what you need to continue to be healthy and successful. Find a way to have a happy quarantine. Here’s a bunch more ideas. There are also a million articles on this topic (these are my favorite four out of the 40+ I’ve seen).
  • If you are already working in an essential industry, are a valiant healthcare worker, etc., definitely keep doing that.
  • If you have the skills to contribute to vaccines, antivirals, etc... obviously do that.
  • Rest a lot if you feel sick. Do what you need to do to self-care and look after your mental health.
  • Contact your government decision-makers and let them know you support the shutdown and value public health. Now is an unusually important time to make your voices heard and convince others to do the same!
  • If you are a publicist, social media influencer, or have celebrity contacts, consider getting them onboard with maintaining public support.
  • If you have social media experience and/or online advertising experience, consider helping out with some social media campaigns.
  • Research one of my research ideas for coronavirus and publish your findings.
  • If you have expertise in data science or forecasting, besides trying to work on these research questions, it seems worth throwing significant time to various forecasting efforts like Metaculus’s Pandemic Questions, the Good Judgment Open, and/or Kaggle. This could potentially scale to consume a significant amount of EA talent, though it may not be that neglected.
  • If you have deep learning and image recognition experience, you could try to join https://www.covid19challenge.eu/
  • Find a project on “Help with Covid”, which also lets you filter by skill. Read through LessWrong and the “Effective Altruism Coronavirus Discussion” FB group. Look through this list of EA approaches. However, be wary of low neglectedness and widespread duplication of work.
  • Spend time helping aggregate and organize information, maybe by making the Coronavirus Tech Handbook nicer and updating Wikipedia.
  • Reach out to your local community, friends, family, and neighbors and make sure they feel supported and are doing okay in this trying time.
  • With the pause in normal work now could be a great time for some personal and organizational reflection. Self-evaluation can pay big dividends in the long-term. Perhaps now you have time to re-evaluate long-term strategy, evaluate hiring practices, management style, employee morale, team culture, etc.


80,000 Hours also suggests donations for COVD-19 relief: Center for Health Security at John Hopkins, Gates Foundation COVID-19 Funds, and the Center for Global Development.


You can now help fight COVID using your laptop’s spare cycles via Folding@Home.

A Glance at The Latest Situation

Things are still getting bad quickly. See FT’s latest graph reprinted below. ...As Justin Wolfers puts it: “Project the U.S. line forward just 7 days, and we'll be at 10,000 deaths in total. Project it forward a week after that, and we'll be at 10,000 per day.” (Hopefully we’ll have flattened the curve a bit since then.)

The case numbers look bad, but at least the second derivative (growth in growth) shows some good news. The New York Times reports:

However, the situation still is dire: “The rate of increase in cases [in New York City] is far higher for the number of cases than it was in Wuhan or Lombardy, once they had reached similar numbers of cases. Other metropolitan areas, like Detroit and New Orleans, stand out as places where a coronavirus outbreak might escalate quickly without preventive measures. The Seattle and San Francisco areas, in contrast, seem to have made serious progress in flattening the curve.”


Here’s a very good visualization of percent of people infected by region in the world.


Note that case numbers are related to testing numbers, differences in who gets tested, and how cases, tests, and deaths are reported - and these all can differ country-to-country. This might be why Germany has such a low death-per-case rate. Italian data may also be underreported. Same with Spanish data. We should be prepared for the data to be a bit wonky and for comparisons to not be entirely apples-to-apples.

For example, I’ve been watching China-related COVID-19 reporting with a lot of anticipation… they seem to be doing very well at containing the virus and could become a model for the rest of the world. However, there’s also a lot of disinformation and misinformation around China. There’s good reason to distrust their numbers. There’s also good reason to distrust the distrust of their numbers. Some Chinese doctors who tested negative for coronavirus have later tested positive. ...One thing that is easy to verify: China re-closes all cinemas over fear of a second coronavirus outbreak.

Similarly, I share Scott Alexander’s deep confusion over what the hell is going on in Japan, Iran, Nigeria, and Mexico:

Japan should be having a terrible time right now. They were one of the first countries to get coronavirus cases, around the same time as South Korea and Italy. And their response has been somewhere between terrible and nonexistent. A friend living in Japan says that “Japan has the worst coronavirus response in the world (the USA is second worst)”, and gets backup from commenters, including a photo of still-packed rush hour trains. [...]

But actually their case number has barely budged over the past month. It was 200 a month ago. Now it’s 1300. This is the most successful coronavirus containment by any major country’s, much better than even South Korea’s, and it was all done with zero effort.

The obvious conclusion is that Japan just isn’t testing anyone. This turns out to be true – they were hoping that if they made themselves look virus-free, the world would still let them hold the Tokyo Olympics this summer.

But at this point, it should be beyond their ability to cover up. We should be getting the same horrifying stories of overflowing hospitals and convoys of coffins that we hear out of Italy. Japanese cities should be defying the national government’s orders and going into total lockdowns. Since none of this is happening, it looks like Japan really is almost virus-free. The Japan Times is as confused about this as I am. [...]

Also, what about Iran? The reports sounded basically apocalyptic a few weeks ago. They stubbornly refused to institute any lockdowns or stop kissing their sacred shrines. Now they have fewer cases than Spain, Germany, or the US. A quick look at the data confirms that their doubling time is now 11 days, compared to six days in Italy and four in the US. Again, I have no explanation. [...]

The third world …is in really deep trouble, isn’t it?

The numbers say it isn’t. Less developed countries are doing fine. Nigeria only has 65 cases. Ethiopia, 12 cases. Sudan only has three!

But they probably just aren’t testing enough. San Diego has 337 diagnosed cases right now. The equally-sized Mexican city of Tijuana, so close by that San Diegans and Tijuanans play volleyball over the border fence, has 10. If we assume that the real numbers are more similar (can we assume this?), then Mexico is undercounting by a factor of 30 relative to the US, which is itself undercounting by a factor of 10 or so. This would suggest Mexico has the same number of cases as eg Britain, which doesn’t seem so far off to me (Mexico has twice as many people). [...]

Nigeria and Mexico and so on make me confused in the same way as Japan – why aren’t they already so bad that they can’t hide it? If the very poorest countries in sub-Saharan Africa were suffering a full-scale coronavirus epidemic, would we definitely know? In Liberia, only 3% of people are aged above 65 (in the US, it’s 16%). It only has one doctor per 100,000 people (in the US, it’s one per 400) – what does “hospital overcrowding” even mean in a situation like that? I don’t think a full-scale epidemic could stay completely hidden forever, but maybe it could be harder to notice we would naively expect.

Tyler Cowen also asks “Where does all the heterogeneity come from?”: “Can anyone shed light on why the death rate is not higher in Iceland? Is it that the death rate is about to burst a week from now? [...] Similarly, Sweden hasn’t restricted public life very much and they do not seem to be falling apart? [...] It is possible that Cambodia, Thailand, and Vietnam still will be hit hard, but so far the signs do not indicate as such. Warm weather may play a positive role, though that remains speculative. The latest weather paper appears credible and indicates some modestly positive results. Of course weather won’t explain the relative Icelandic and Swedish success, if indeed those are truly successes.”

Similarly, why aren’t a ton of Swedes on their way to being dead?

Maybe it’s still just a matter of time? Vox argues that Mexico’s coronavirus-skeptical president is setting up his country for a health crisis and Japan’s coronavirus crisis may be just beginning.

...Also, beware Goodhart’s Law in these metrics - originally US states were strongly incentivized to underreport, but now that relief is tied to caseload, they are strongly incentivized to overreport.


There is now data on ICU beds by US county.

...So Just How Bad Could This All Get?

"From Spain to Germany, Farmers Warn of Fresh Food Shortages" warns a Bloomberg article, mainly due to fewer workers being available to pick fruit. I’m pretty skeptical of COVID-related food shortages, but I still think it is important to monitor and be on the lookout. The NYTimes reports that there is still plenty of food in storage and supply chains are currently getting replenished just fine.

Gaze into the Crystal - The Latest Modeling and Forecasting

A Stanford team produces a stochastic model to forecast a "lightswitch approach” to lockdowns, where we alternatively lockdown and un-lockdown to continually keep the case load manageable enough to not overwhelm hospitals.

Vipul Naik estimates when we will get out of lockdown. He thinks the strict “shelter in place / go out for emergencies only” might get relaxed back to “most things closed”-level lockdown by mid-June and to the “most things open except large groups still banned”-level lockdown by summer 2021 and back to business as usual by summer 2022.

A survey of 18 epidemiologists say COVID-19 will cause approximately 195,000 deaths in the US and that a “second wave” of the virus is likely to occur between August and December.

Dr. Fauci predicts over 100,000 Covid-19 deaths in the United States.

A hospital-specific model tries to use more specific information about shortages. And another model, not tied to specific hospitals, but still modeling the impact of PPE shortages.

Now Let’s Talk Policy Response

Harvard Business Review outlines some key lessons from Italy:

  1. “Recognize your cognitive biases. In its early stages, the Covid-19 crisis in Italy looked nothing like a crisis. [...] Threats such as pandemics that evolve in a nonlinear fashion (i.e., they start small but exponentially intensify) are especially tricky to confront because of the challenges of rapidly interpreting what is happening in real time.”
  2. “The most effective time to take strong action is extremely early, when the threat appears to be small — or even before there are any cases. But if the intervention actually works, it will appear in retrospect as if the strong actions were an overreaction. This is a game many politicians don’t want to play.”
  3. “The systematic inability to listen to experts highlights the trouble that leaders — and people in general — have figuring out how to act in dire, highly complex situations where there’s no easy solution.”
  4. “Avoid partial solutions. A second lesson that can be drawn from the Italian experience is the importance of systematic approaches and the perils of partial solutions. The Italian government dealt with the Covid-19 pandemic by issuing a series of decrees that gradually increased restrictions within lockdown areas[... I]t backfired for two reasons. First, it was inconsistent with the rapid exponential spread of the virus. [...] Second, the selective approach might have inadvertently facilitated the spread of the virus.”
  5. “Consider the decision to initially lock down some regions but not others. When the decree announcing the closing of northern Italy became public, it touched off a massive exodus to southern Italy, undoubtedly spreading the virus to regions where it had not been present.”
  6. “An effective response to the virus needs to be orchestrated as a coherent system of actions taken simultaneously.”
  7. “These rules also apply to the organization of the health care system itself. Wholesale reorganizations are needed within hospitals (for example, the creation of Covid-19 and non Covid-19 streams of care).”
  8. “Finding the right implementation approach requires the ability to quickly learn from both successes and failures and the willingness to change actions accordingly. Certainly, there are valuable lessons to be learned from the approaches of China, South Korea, Taiwan, and Singapore, which were able to contain the contagion fairly early. But sometimes the best practices can be found just next door. Because the Italian health care system is highly decentralized, different regions tried different policy responses. The most notable example is the contrast between the approaches taken by Lombardy and Veneto, two neighboring regions with similar socioeconomic profiles.”
  9. Good policies: “Extensive testing of symptomatic and asymptomatic cases early on. Proactive tracing of potential positives. If someone tested positive, everyone in that patient’s home as well as their neighbors were tested. If testing kits were unavailable, they were self-quarantined. A strong emphasis on home diagnosis and care. Whenever possible, samples were collected directly from a patient’s home and then processed in regional and local university labs. Specific efforts to monitor and protect health care and other essential workers.”
  10. “It is especially important to understand what does not work. While successes easily surface thanks to leaders eager to publicize progress, problems often are hidden due to fear of retribution, or, when they do emerge, they are interpreted as individual — rather than systemic — failures.”
  11. “Collecting and disseminating data is important. Italy seems to have suffered from two data-related problems. In the early onset of the pandemic, the problem was data paucity. More specifically, it has been suggested that the widespread and unnoticed diffusion of the virus in the early months of 2020 may have been facilitated by the lack of epidemiological capabilities and the inability to systematically record anomalous infection peaks in some hospitals.”
  12. “More recently, the problem appears to be one of data precision. In particular, in spite of the remarkable effort that the Italian government has shown in regularly updating statistics relative to the pandemic on a publicly available website, some commentators have advanced the hypothesis that the striking discrepancy in mortality rates between Italy and other countries and within Italian regions may (at least in part) be driven by different testing approaches.”
  13. “In an ideal scenario, data documenting the spread and effects of the virus should be as standardized as possible across regions and countries and follow the progression of the virus and its containment at both a macro (state) and micro (hospital) level.”


Rhode Island essentially declares war on New York City:

Rhode Island police began stopping cars with New York plates Friday. On Saturday, the National Guard will help them conduct house-to-house searches to find people who traveled from New York and demand 14 days of self-quarantine.

“Right now we have a pinpointed risk,” Governor Gina Raimondo said. “That risk is called New York City.”


WHO is very cagey / weird about Taiwan. WHO Director General, Bruce Aylward is asked about Taiwan’s membership in WHO… he responds by hanging up and then pretending to have not heard the question. Taiwan is not a member of WHO due to China’s insistence that Taiwan is a part of China.

A Bit About Life Under Quarantine

Americans have been changing their plans a lot:


Wild... “Our estimate is that about 12-13% [of churches in the US] were still open to in-person worship as of this week.”

The Psychologists Also Have Something to Say About This

For those studying the social psychology of the pandemic, there is now a research tracker.


The EA Australia Research Collaboration is running surveys to help policymakers with decision making about how to allocate resources to tackle COVID-19:

We doing something quite different from other ongoing surveys. Most of these are about country level comparisons and not about understanding behavioural drivers. For example, we can report things by location and demographic, but also why people are not doing the behaviours, e.g., 75% of males between 30 and 40 are social distancing but only 50% of males between 20-30. At only 55% adherence, the inner west region reports the lowest amount of social distancing. The main capability barriers are commuting and desire to see friends. 97% of those surveyed are always washing their hands, suggesting that this need no longer be a key communication target. [...]

Please consider joining us in collaboration. You can contribute by:

  • Helping to disseminate the survey via social networks or panel data.
  • Reaching and helping policy makers in your country with the data we collect
  • Helping to modify our report template to provide useful and interesting information to policy makers.
  • Developing reports and doing analysis for policy makers
  • Providing us with feedback based on your discussion data collection
  • Helping with write up and dissemination when we seek to publish this work

If you contribute to this project in any significant way then you will be recognised on all outputs and be an author on any subsequent paper. The bar for recognition will be relatively low (perhaps ~5 hours of work).

If You Still Own Envelopes, Check Their Backs - Here’s the Latest Cost-Benefit Analysis

The IGM Forum regularly polls economists about US economic policy. Economists rarely agree. But this time, economists are unanimous that the large contraction in economic activity is worth fighting coronavirus, we should not abandon the severe lockdowns, and the government should invest more in policy response.

“A comprehensive policy response to the coronavirus will involve tolerating a very large contraction in economic activity until the spread of infections has dropped significantly.”

“Abandoning severe lockdowns at a time when the likelihood of a resurgence in infections remains high will lead to greater total economic damage than sustaining the lockdowns to eliminate the resurgence risk.”

“Optimally, the government would invest more than it is currently doing in expanding treatment capacity through steps such as building temporary hospitals, accelerating testing, making more masks and ventilators, and providing financial incentives for the production of a successful vaccine.”

A study using data from the 1918 Flu found that greater economic growth was connected with lockdowns as opposed to the opposite. Faster social distancing also saved a lot of lives during the 1918 Flu.

Now Just What are the Tech Overlords up to?

Apple launches a COVID-19 screening tool. According to TechCrunch: “The site is pretty simple, with basic information about best practices and safety tips alongside a basic screening tool which should give you a fairly solid idea on whether or not you need to be tested for COVID-19.”


The New York Times calls for “Big tech needs to rapidly build and scale a cloud-based national ventilator surveillance platform which will track individual hospital I.C.U. capacity and ventilator supply across the nation in real-time. Such a platform — which Silicon Valley could build and FEMA could utilize — would allow hospitals nationwide to report their I.C.U. bed status and their ventilator supply daily, in an unprecedented data-sharing initiative.” Probably already being done by 18 different groups now, but might still be worth exploring?


Zuckerberg donates $25M to the Gates Foundation to fight coronavirus. It’s an accelerator to find new possible antiviral drugs and total funding for the accelerator is now at $125M.

Not to be outdone, Google pledges to donate $800 million and 3 million face masks in an effort to combat the coronavirus… however, over 75% of this fund comes in the form of Google ad grants and cloud computing credits.

Lastly, Mayo Clinic and Amazon launched a collaboration to increase COVID-19 testing and vaccine development: “The private industry effort, spearhead by Mayo Clinic's John Halamka, M.D. and other industry leaders, plans to leverage the strengths of healthcare organizations, technology companies, non-profits, academia, and startups to provide a focused response to the coronavirus outbreak.” It’s not super clear what this means but I’m glad it’s happening.


Maybe we shouldn’t be using Zoom? Consumer Reports reports “Zoom Calls Aren't as Private as You May Think”. Engadget reports “Zoom happens to be a privacy nightmare with a terrible security track record” and that “Zoom collects your physical address, phone number, your job title, credit and debit card information, your Facebook account, your IP address, your OS and device details, and more and traffics that data with whomever it's doing business with”.


Predictably, Edward Snowden warns about the dramatic increase of tech surveillance.

And How Do We Get Out of this Mess? Vaccines, Treatments, Testing, Tracing, etc.

US testing is no longer increasing exponentially:

...But I’m told this is a game changer: Abbot launches molecular point-of-care test to detect novel coronavirus in as little as five minutes. They’ve already received FDA emergency use authorization.


Dr. Fauci outlines ambitious plan to scale up COVID-19 vaccine. Looks like the proposal is to start ramping up production of a vaccine while the candidate is still in Phase II clinical trials. This risks spending a ton of money producing a vaccine that ultimately might not get approved by the FDA. Dr. Fauci has suggested hundreds of millions in incentives to make this work.


The Washington Post reports that “hVIVO, a clinical research group in London, has attracted more than 20,000 volunteers willing to be infected with tamer relatives of the virus that causes Covid-19 in exchange for a fee of £3,500 ($4,480).”


To stop COVID-19, test everyone, repeatedly: “We propose an additional intervention that would contribute to the control of the COVID-19 pandemic and facilitate reopening of society, based on: (1) testing every individual (2) repeatedly, and (3) self-quarantine of infected individuals. By identification and isolation of the majority of infectious individuals, including the estimated 86% who are asymptomatic or undocumented, the reproduction number R0 of SARS-CoV-2 would be reduced well below 1.0, and the epidemic would collapse. This testing regime would be additive to other interventions, and allow individuals who have respiratory symptoms due to other causes to return to work, but would have to be maintained until a vaccine becomes available. Unlike sampling-based tests, population-scale testing does not need to be very accurate: false negative rates up to 15% could be tolerated if 80% comply with testing, and false positives can be almost arbitrarily high when a high fraction of the population is already effectively quarantined.”

And Now a Word From the Lamestream Media

Kelsey Piper is the best. Vox, and other media outlets have not apologized for continually downplaying the coronavirus and calling out people as fearmongering. For example, on 13 February, Vox made fun of the prescient tech industry, deriding them with “Although public officials in the area say the virus is contained for now, some in the tech industry fear the virus will spread out of control.” Kelsey Piper, Vox Future Perfect author, is at least willing to admit she made a mistake. No one else seems to be… yet Kelsey is getting all the hate for it. :(

(I also made a mistake by privately stockpiling on 8 February, but not telling anyone of my fears out of a meta-fear of looking like some dorky prepper. I also apologize for this.)

The Non-Profit Impacts

American non-profits that (a) existed before 1 March 2020, (b) have fewer than 500 employees, and (c) keep staff on payroll can get what might amount to free money from the government. This deserves urgent further investigation.

Don’t Forget About the Nonhumans!

Amid continued panig egg buying, US grocers boosted egg orders by as much as six times normal and USDA relaxes rules to allow older eggs to make grade.

Vietnam’s prime minister, Nguyen Xuan Phuc, has asked the country’s agriculture ministry to draft a directive to stop illegal trading and consumption of wildlife over fears it spreads disease.

Your Regular Dose of WTF

Fun (Online) Distractions, Because We All Still Need to Enjoy Life

The Rotterdam Philharmonic Orchestra delivers Beethoven's 'Ode to Joy' with 19 musicians playing their parts from their homes.

Cute dog undertakes a sisyphean task of constantly fetching the ball, only for it to roll back down the hill.

Dad learns how to Tik-Tok dance.


Thanks to Elizabeth Van Nostrand, Robert Krzyzanowski, and countless others for assistance in aggregating and editing this list.


Coronavirus: California case growth

29 марта, 2020 - 19:14
Published on March 29, 2020 4:14 PM GMT

In this post, I try to understand the case growth rate for coronavirus cases in California, and try to address questions such as:

  • How long will the case count continue to grow?
  • At what level will the case count stabilize?
  • To what extent will we be able to infer from the data whether level 2 restrictions were sufficient, or level 3 restrictions were necessary, to stop or significantly slow down case growth? The "level 2" and "level 3" jargon are from my previous post.

Skip to answers and lessons for my (incomplete and tentative) answers.

A simple model from true cases to confirmed cases to deaths or recoveries The model

For simplicity, I will use the term "true cases" only for cases that are eventually symptomatic. I expect that most asymptomatic cases won't get diagnosed, and therefore won't count in the number of confirmed cases either, so this seems a reasonable approximation for the time being. However, if incorrect, this could cause estimates to be off by a factor of two.

The simplistic model identifies the following flow:

  1. Get infected
  2. Start showing symptoms
  3. Get a test
  4. Get test results
  5. Recover or die

Technically, 5 can happen before 3 or 4; the logical dependencies are 1 -> 2 -> 5 and 1 -> 2 -> 3 -> 4. It's also possible (and probably more likely) that 5 happens after 3 but before 4.

To keep this post focused, I will not discuss 5 here, though it's obviously very important.

Time lags in the model (1 -> 2 -> 3 -> 4)

The total time lag from 1 to 4 shows up as the lag between any trend change in the number of true cases, and the corresponding trend change in the number of confirmed cases. The more accurately we can estimate and measure this total time lag, the more accurately we can relate the timing of social distancing measures and the timing of case growth flatlining. Herei s what I know:

  • The 1 -> 2 lag is in the range of 2 to 14 days, according to CDC. I'll use a median estimate of 1 week.
  • The 2 -> 3 lag depends on the queue/backlog for tests. It looks like there is no single queue for tests, but rather, different kinds of cases are in different queues (those showing severe symptoms or those who need to do essential work may get a priority for being tested). For simplicity, I'll use a median estimate of 1 week. See here for reasonably up-to-date information on the experience of getting tested.
  • The 3 -> 4 lag seems to be between 5 and 10 days. Again, I'll use a median estimate of 1 week.

Using median estimates for each suggests that there is a lag of 3 weeks between trend changes in true cases and trend changes in confirmed cases. If this 3 weeks were precise, then the trend in confirmed cases will be a 3-week time translation of the trend in true cases. In practice, however, because each transition has a variable time range, varying across individuals, the true time range is more like 2 to 6 weeks. And rather than a crisp time translation, we see a fuzzy smear -- even if true cases flatline immediately after the escalation from level 2 to level 3 (flexible lockdown), the confirmed case count will show no such sharp trend change, instead showing a leveling off over time.

Looking at the California data Description of the data

The California Department of Public Health publishes daily releases on coronavirus case counts as of the previous date. The reports have always included data on the number of confirmed positive cases and the number of deaths. Starting with the release for March 18 (published March 19), the release includes data on the total number of tests and the total number of test results returned.

I put the data together in a spreadsheet where I added columns for the daily increments to each value, as well as some percentages and comparisons of interest. A few notes:

  • There are two dates with sharp changes to the incremental number of confirmed positive cases (i.e., the "second derivative" of the confirmed positive case count is high; see column E for confirmed positive cases, column I for the first derivative and column O for the second derivative): the transition from March 18 to March 19, and the transition from March 25 to March 26. Outside of these days, the second derivative is low; the growth seems to be closer to piecewise linear or quadratic than exponential. The increase from March 18 to March 19 may be due to more testing capacity -- it's hard to say because we have test counts only starting March 18. The increase from March 25 to March 26 is off by a few days from an increase in the number of test results. However, if there is a lag between test results and confirmed cases showing up, that might explain the jump.

  • The total number of tests jumped a lot from March 23 to March 24 (see column D for the number of tests and column G for the first derivative). Looking at language in the CDPH report pages, this seems to be because tests from some state and local health labs that were previously not included have started getting included.

Extrapolating the number and timeline of confirmed positive cases for people already tested

Let's go back to our simple model:

  1. Get infected
  2. Start showing symptoms
  3. Get a test
  4. Get test results
  5. Recover or die

It is quite hard to measure 1 and 2 from the data we have, but we can shed light on 3 and 4 based on the data collected here.

First, as noted in the previous section, the data seems consistent with a 3 -> 4 lag of 5 days or a little more. Specifically, the number of test results on a given day is around 75% to 90% of the number of tests about five days before that. This is consistent with test results taking five days, but some results getting delayed. See column M.

However, as the number of tests has increased quite a bit recently , the lag might increase a lot in the next few days if processing capacity has not kept pace.

Second, we see that right now, the majority of tests don't yet have results (i.e., there is a lot in the 3 -> 4 transition). Therefore, even assuming that there are no more true cases coming through 1 -> 2 -> 3 any more, there's still a lot in 3 -> 4 and much of it may be confirmed positive.

Third, at least so far, the cumulative confirmed positive rate (confirmed positive cases as a percentage of test results; see column L) has been going up, albeit slowly. The incremental confirmed positive rate (incremental confirmed positive cases as a percentage of incremental test results; see column K) is more noisy, but is also generally higher in recent days than it was in the beginning. The increase in confirmed positive rate could be because (a) the selection of who takes the test is getting more precise, as people better understand the right symptoms, flu test screening is instituted, and test criteria are improved, or (b) the false negative rate of tests is reduced as tests become more accurate.

With all these, we can make the following loose predictions:

  • We expect to see results for about 64,000 currently pending tests in the next 5 to 7 days, assuming test processing capacity keeps pace.

  • If the confirmed positive rate of the remaining tests matches that of the tests so far, we will see about 16,514 confirmed positive cases from the people who have already been tested (cell N17).

  • Here is an argument that the confirmed positive rate will be dramatically lower for the still-pending tests, even though it's been increasing so far: We have just recently hit the point where the people getting tested now are testing "too late" to have actually gotten the disease, because this is just about the right amount of lag after we went to level 2 or level 3.

  • Here is an argument that the confirmed positive rate will be higher for the still-pending tests: Since the confirmed positive rate has been generally increasing, it may be better to extrapolate from the confirmed positive rate of the last 2 or 3 days.

Based on these considerations, I estimate that, just from the people who have gotten tested so far, we should expect a total of 10,000 to 40,000 cases in California. This is inclusive of the already-diagnosed 4,643 cases. I also expect that, if testing capacity keeps pace with the number of tests done, we will hit this number (somewhere between 10,0000 and 40,000) by around Friday, April 3, along with the number of test results getting to equal or exceed the current total number of tests (~89,000).

Further, I expect that (again assuming that test processing capacity roughly keeps pace) we will see another sharp increase in the incremental confirmed positive case count in the transition from March 28 to March 29 or March 29 to March 30. This will lag by about 5 days the sharp increase from March 23 to March 24 in the total number of tests. More specifically, I expect that the incremental number of confirmed positive cases will go up from its current daily value of ~800 to a few thousand.

Thinking about the transitions till testing (1 -> 2 -> 3)

The data here doesn't give a clear idea of how the transitions from 1 to 2, or from 2 to 3 are proceeding. Nonetheless, it may offer some clues. So first, let's backtrack and think: let's say California going to level 2 or level 3 did in fact effectively stop coronavirus in its tracks. What should we see?

Ideally, we should see the number of people with coronavirus getting the test drop a lot. However, that doesn't necessarily mean that the total number of people getting the test drops, because many people who don't have the disease may also start getting tested, causing the total number of people getting tested to increase. So, more accurately, we should see one of these:

  • A drop in the incremental number of tests each day.
  • A drop in the confirmed positive rate on tests (but this metric is available at a further lag of 5 to 7 days).

Unfortunately, we aren't seeing the second yet. As for the first, the transition data from March 26 to March 27 suggests that yes, we are seeng a drop in the incremental number of tests (the increment went down from 10,600 to 1,200). But that's just one day of data. If we see a similar drop persist, that might mean that we are finally seeing the lagged effects of escalating to level 2 or level 3. A week after that we should see a drop in the growth rate of confirmed positive cases.

Is the data good enough to know whether level 2 is sufficient, or whether we need level 3?

My rough estimate is that California achieved level 2 starting around March 11 to March 13, and escalated to level 3 around March 17 to March 19. The gap is about one week. This is a really small gap, and is dwarfed by the range of variation in the time lag. If case counts level off in the next one or two weeks, we won't have good enough data to say whether level 2 was sufficient, or the escalation to level 3 was necessary.

Of course, while aggregate data may not say much, it is still possible that more detailed analysis of individual cases will answer the question. Specifically, we would need to identify the number of individual cases where we expect that they got the infection in the time period when California was level 2. However, because of the long period between getting exposed and showing symptoms, we may have a large number of cases where we are pretty uncertain.

Answers and lessons Answers

I summarize the predictions from this post here.

  • The super-optimistic scenario is that almost all people who had the disease are already tested, and confirmed positive rates for the pending tests will be lower than those for the tests so far.

  • In this super-optimistic scenario, I expect something like 10,000 confirmed cases and, assuming test processing capacity keeps pace, I expect the number to be hit by around April 3. For comparison, there are currently 4,643 cases.

  • My estimate range for the number of confirmed positive cases from people already tested is 10,000 to 40,000. With the optimistic (but not super-optimistic) assumption that almost all people who had the disease are already tested, I expect us to hit this number by around April 3, after which the growth rate of confirmed positive cases will slow down to a trickle.

  • Given the huge time lags and variation in time lags, it will be hard, even after case growth stops, to know whether level 2 was sufficient or level 3 was neceessary to arrest case growth.

  • Cutting down time lags (as well as variation in time lags) is crucial to being able to reason clearly about cause and effect between social distancing measures and infection growth rates.

  • In particular, cutting down the time spent waiting to get a test (the 2 -> 3 transition), and cutting down the time taken to process test results (the 3 -> 4 transition), is absolutely critical.

  • Better heuristics for people to identify themselves as needing to get tested, even before they start feeling sick, would be great (it would speed up the 1 -> 2 transition). For instance, if loss of smell is an early indicator, even before a person otherwise feels sick, that could help people get 1 -> 2 faster.

  • Getting more detailed data on each case, to gauge the expected true start date of infection, is very important to be able to determine the true growth rate of an infection. I hope some people are doing this, because the publicly available aggregate statistics are not of much use for that.

  • I personally found it more helpful to model confirmed case trends as linear, quadratic, or piecewise linear/quadratic than exponential. This is because at least at present, the bottlenecks are around testing capacity, which is growing linearly or quadratically, not exponentially.


Chris Masterjohn on Coronavirus, Part 1

29 марта, 2020 - 14:00
Published on March 29, 2020 11:00 AM GMT

Chris Masterjohn is a nutritionist who has some advice on supplements to take to help protect against Covid-19, and some to avoid. The raw advice is available for free, but the full report with explanation and references costs $10. I bought a copy.

Should we trust him? On the one hand, "nutritionist" is not a profession I necessarily hold much respect for (it's not a protected term). Nor do I tend to think highly of people emphasizing that they have a PhD. Also, his website looks super sketchy to my eyes. Also also, something that costs money gets fewer eyeballs than something free, and so mistakes are less likely to be discovered.

(Only the last one of those should be considered a problem with the report; the rest are just priors.)

On the other hand, Chris previously turned me on to zinc so he has some credibility with me. Also, although I'm out of my depth in actually evaluating the science, I do think I have a decent bullshit detector, and the report is superficially very convincing to me.1 I get the sense that he actually really knows what he's doing, is weighing the evidence appropriately and making claims that are justified by it. He admits to uncertainty, and some of his recommendations are "this probably won't help, but just in case"; but he does speak precisely enough to be falsifiable. This doesn't mean he's right, of course. But I think he's worth some people paying some attention to.

My intention here is mostly to summarize his reasoning, in more detail than is on the linked page but less detail than is in the full report. You can make up your own minds. Much of it I haven't even checked how actionable it is right now (i.e. are these things available in stores or for delivery, and how much do they cost). I do have some questions and commentary of my own that I've scattered about, possibly not always well-flagged.

The report makes twelve recommendations:

  • Four "essentials": elderberry, nutritional zinc, ionic zinc, and copper.
  • Four "optional add-ons": garlic or stabilized allicin, echinacea, vitamin C, and N-acetyl-cysteine. Chris is taking all of these except vitamin C, and he plans to take a low-dose supplement of that if he can't get fresh food.
  • Four "things to limit or avoid": these are much less pithy. "Don't take high doses of vitamins A or D" (but don't get deficient either); "Limit Calcium and Don't Use Calcium Supplements That Aren't Balanced by Phosphorus"; "Don't Use Monolaurin"; "Don't Use High-Dose Vitamin C, Pelargonium Sidoides (Umcka), or Bee Propolis".

I'm not convinced twelve recommendations is the natural grouping for this, but there we are. In this post I'm going to focus on the "essentials"; my current plan is to do the rest in future posts.

So far, I've followed this advice to the extent of:

  • I've stopped taking daily vitamin D supplements, and advised my partner to do the same. (Somewhat to my embarassment, as they'd only started taking them on my recommendation. I've been taking it 5x/week for years.) He says not to supplement with vitamin D at all, but he also says to get normal amounts from sunshine and vitamin D-rich foods, and we're not doing that. So maybe we should take one a week instead of one a day.
  • I bought a bulb of garlic, but I haven't done anything with it yet.
  • I took one of the zinc capsules that came free with my last order of zinc lozenges. Then I had a bad gastrointesinal day. Possibly unrelated, but they also had something of an unpleasant smell that I don't know whether it was normal. I haven't tried again. Maybe I will. They're 50mg, which is more than Chris recommends at one time.

If you're going to follow any of it yourself, you should at least read his public post, and ideally also the report.

General info: ACE2

SARS-CoV-2 (the virus that causes the disease Covid-19) is not like the common cold or the flu, and things which work against those may not be helpful here. On the other hand, it's very like SARS-CoV (the virus that causes SARS). The genomes are 80% identical, and "the majority of its proteins are 85-100% homologous, with an average homology of 87%".

(Question: That seems like a really low average, given that range?)

The two main things the report focuses on are ACE2 and interferon.

ACE2 is an enzyme used in regulating blood pressure. SARS-CoV-2 enters our cells by docking to ACE2. It has this in common with just two other human coronaviruses, SARS-CoV and HCoV-NL63. So heightened levels of ACE2 could increase susceptibility to SARS-CoV-2.

(By comparison, the common cold is mostly caused by rhinoviruses, and most of those dock to ICAM-1. Some colds are caused by coronaviruses, but those dock to aminopeptidase N or sialic acid. The flu docks to sialic acid. So if something protects against those by preventing them from docking, it's likely to have no effect on Covid-19.)

I'll talk about interferon in a future post, because it's not relevant to any of the "essentials".

Essential: Elderberry

"In rhesus monkey kidney cell culture, elderberry has virucidal, anti-plaque, anti-replication and anti-attachment activity toward HCoV-NL63". Most of the effect seems to come from caffeic acid. That binds directly to ACE2, which suggests elderberry would be similarly effective against SARS-CoV and SARS-CoV-2.

(Question: is that in vitro? Doesn't seem very reliable in general, but maybe the specific mechanism makes it moreso.)

As a bonus, elderberry is also effective against avian infectious bronchitis virus through compromising the lipid envelope. Since all coronaviruses have one of those, that effect might generalize to SARS-CoV-2.

Other foods include caffeic acid, but only black chokeberries have anything like a comparable amount. And elderberry extract is the thing that's been actually studied, so that's what Chris recommends.

There are studies using elderberry in humans to treats colds, the flu, and cardiovascular disease, but Chris doesn't mention their results. He just uses them to determine a safe dose.

Essential: Nutritional zinc

This is zinc from food or most supplements (including tablets or capsules).

Zinc "inhibits three proteins required for SARS-CoV replication: papain-like protease-2, 3CL protease, and helicase." So it probably inhibits the homologous proteins in SARS-CoV-2.

(Question: how similar do proteins need to be for this to be a decent guess? This suggests proteins called "homologous" might be only 40% similar in sequence. If I guess that "homologous" means this is likely to be a decent guess; and that these ones are >85% similar (based on base rates of similarity between the viruses)… that suggests it's probably a pretty good guess? But I'm not at all cofident in this thinking.)

So we should try to deliver zinc to the relevant tissues. What are those?

The infection would begin somewhere between the nose or throat (where the virus mostly enters our body) and lungs (where it primarily infects), wherever the virus first encounters ACE2. There are two papers trying to answer this question, and they give different opinions.

(Question: this seems to assume that the virus doesn't first infect something other than the lungs, and then move on to them. Is that a safe assumption? I would guess so.)

Hamming et al (2004)2 suggests that the virus wouldn't find any ACE2 until it reached the lungs. They did find ACE2 in the oral and nasal mucous membranes, but on the wrong side to be any use to the virus.

Xu et al (2020)3 argues that ACE2 is highly expressed through the surface of the mouth, especially the tongue.

The two used different methods. Xu had better resolution, down to single-cell. But Hamming could tell where on the cells the ACE2 was expressed. The second thing matters, and the first doesn't. Xu's results are what Hamming would have predicted, they just aren't relevant. (The symptoms are also what Hamming would predict: a cough suggests lung infection, and is present. A runny nose or other cold symptoms would suggest throat infection, but they aren't present.)

We don't have a specific mechanism to target the lung with zinc. We just have to take it orally in the normal way (that is, in food or supplements) and let it be distributed there.

Chris recommends 7-15mg of zinc four times a day, away from sources of phytate ("whole grains, nuts, seeds, and legumes") which can inhibit zinc intake.

(At one point he says 10-15mg, and at one point 7-10, but I think this is just bad proofreading. Mostly he says 7-15.)

Conventional wisdom says we can't absorb nearly that much, but Chris thinks we just need more dakka: "the relevant studies have been limited to less than 20 mg/d. Supplementation with 100 mg/d zinc sulfate has been shown to more than double total zinc absorbed from 4.5 to 10.5 mg/d, while decreasing the fraction absorbed from 43% to 9%."

At such high doses, side effects have been observed. "Zinc at 50 mg/d has been shown to lower superoxide dismutase, and at 85 mg/d increased self-reported anemia. Both of these could probably have been averted by proper balancing with copper, which is addressed in the section below. However, the increased need for copper at high zinc intakes reflects increased expression of metallothionein, which can bind to many positively charged metals besides copper." I confess I'm not sure what this "however" is meant to mean. It kind of sounds like "so we still probably shouldn't go that high", but then we go that high anyway. I'm a bit concerned about this.

(I also confess that I have no idea what superoxide dismutase is.)

If you take zinc, you should balance it with copper.

Essential: Ionic zinc

This specifically means zinc from the kind of lozenges that work for a cold, or failing that from an ionic zinc throat spray.

(The mechanism that makes this work against a cold will not help with SARS-CoV-2.)

It delivers ionic zinc to the mouth, nose and throat tissues, like we couldn't do with the lungs. But as discussed above, those probably aren't infected, so this probably won't help. It's included "as a hedge against the possibility" that they are. He takes one a day, and plans to increase that if he gets cold symptoms or Covid-19 symptoms.

(Question: this delivers ionic zinc to the surfaces of these tissues, while we want it on the inside. Will that work?)

I'm not really sure why this is an "essential" instead of an "optional add-on".

Essential: Copper

Copper surfaces work great against coronaviruses. This knowledge is not super helpful, since we are not copper surfaces.

It does suggest that copper ions in our cells might be toxic to the virus. But this has never been well studied.

Like zinc, copper inhibits papain-like protease 2 of SARS CoV. But it's much less effective at it.

The main reason to take copper is to keep the zinc-to-copper ratio acceptable. It should be between 10:1 and 15:1. (At one point he says 2:1 - 15:1, but again, I think that's bad proofreading.)

Like with zinc, he recommends also using a copper spray just in case SARS-CoV-2 infects the throat.

  1. There are some things that kind of smell like bullshit to me. Most notably, I feel like at times, the report goes into a lot of detail on things that aren't super relevant, like the renin-angiotensin system that ACE2 plays a part in. As far as I've seen so far, the precise mechanics of that don't really matter. Meanwhile, a lot of the important claims are speculative - necessarily so, because things are moving too fast to have good evidence here, but speculative all the same. In combination, this can kind of feel like… "throw a lot of impeccably researched, uncontroversial and unimportant science at the reader; then try to sneak in the difficult bits under the radar"? I'm sure there's a term for this that I'm forgetting.

    I don't actually think this is what's happening. My sense is that it's more likely to be bad editing, and I'm not even confident it's that. But it seemed important to note.

    I've also noticed some inconsistencies that I chalk up to bad proofreading. 

  2. Hamming, I. et al. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 203, 631–637 (2004). 

  3. Xu, H. et al. High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. Int. J. Oral Sci. 12, 8 (2020). 


How to study statistical/computer modelling of the current pandemic and its outcomes?

29 марта, 2020 - 11:40
Published on March 29, 2020 8:40 AM GMT

The purpose of this question is NOT to help with the ongoing real research.

Rather, I want to use the excitement of living in a pandemic to learn new stuff, mostly statistics and computer modelling. I noticed that I'm wasting a lot of energy reading clickbait articles and I want to redirect that energy towards something more productive in the long term. (Side-note: generally my self-improvement meta-strategy is redirecting emotional impulses with minimal willpower, rather than imposing self-discipline which in my experience depletes more willpower and is less enjoyable.)

I'm interested not only in health outcomes but also/mostly economic ones.

Resources I have identified so far:

For the sake of other readers who might also be interested, any related resources and advice will be very welcome.

My own background is in C/C++ programming with some Python experience and nearly all of statistics knowledge forgotten. I would prefer my study to focus more on maths, statistics, etc. since it should yield greater proportional gain in knowledge for time invested (since my starting point in computer-related fields is much higher and so my learning curve will be flatter), and also because I'm working as a full-time programmer so I prefer to do other things in my free time.